Academic literature on the topic 'Siamese Neural Models'
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Journal articles on the topic "Siamese Neural Models"
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Kurniasari, Dian, Mustofa Usman, Warsono Warsono, and Favorisen Rosyking Lumbanraja. "Comparative analysis of deep Siamese models for medical reports text similarity." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (December 1, 2024): 6969. http://dx.doi.org/10.11591/ijece.v14i6.pp6969-6980.
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Even though medical reports have been digitized, they are generally text data and have not been used optimally. Extracting information from these reports is challenging due to their high volume and unstructured nature. Analyzing the extraction of relevant and high-quality information can be achieved by measuring semantic textual similarity (STS). Consequently, the primary aim of this study is to develop and evaluate the performance of four models: Siamese Manhattan convolution neural network (CNN), Siamese Manhattan long short-term memory (LSTM), Siamese Manhattan hybrid CNN-LSTM, and Siamese Manhattan hybrid LSTM-CNN, in determining STS between sentence pairs in medical reports. Performance comparisons were conducted using Cosine Similarity and word mover's distance (WMD) methods. The results indicate that the Siamese Manhattan hybrid LSTM-CNN model outperforms the other models, with a similarity score of 1 for each sentence pair, signifying identical semantic meaning.
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Ananthakrishnan, Balasundaram, Ayesha Shaik, Shivam Akhouri, Paras Garg, Vaibhav Gadag, and Muthu Subash Kavitha. "Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network." Diagnostics 13, no. 1 (December 29, 2022): 112. http://dx.doi.org/10.3390/diagnostics13010112.
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The critical structure and nature of different bone marrow cells which form a base in the diagnosis of haematological ailments requires a high-grade classification which is a very prolonged approach and accounts for human error if performed manually, even by field experts. Therefore, the aim of this research is to automate the process to study and accurately classify the structure of bone marrow cells which will help in the diagnosis of haematological ailments at a much faster and better rate. Various machine learning algorithms and models, such as CNN + SVM, CNN + XGB Boost and Siamese network, were trained and tested across a dataset of 170,000 expert-annotated cell images from 945 patients’ bone marrow smears with haematological disorders. The metrics used for evaluation of this research are accuracy of model, precision and recall of all the different classes of cells. Based on these performance metrics the CNN + SVM, CNN + XGB, resulted in 32%, 28% accuracy, respectively, and therefore these models were discarded. Siamese neural resulted in 91% accuracy and 84% validation accuracy. Moreover, the weighted average recall values of the Siamese neural network were 92% for training and 91% for validation. Hence, the final results are based on Siamese neural network model as it was outperforming all the other algorithms used in this research.
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Xiao, Wanghui, and Yuting Ding. "A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification." Symmetry 14, no. 6 (June 12, 2022): 1216. http://dx.doi.org/10.3390/sym14061216.
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Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification.
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Cha, Byung-Rae, and Binod Vaidya. "Enhancing Human Activity Recognition with Siamese Networks: A Comparative Study of Contrastive and Triplet Learning Approaches." Electronics 13, no. 9 (May 1, 2024): 1739. http://dx.doi.org/10.3390/electronics13091739.
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This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR’s growing importance in healthcare, sports, and smart environments, the need for advanced models capable of accurately recognizing and classifying complex human activities has become paramount. Addressing this, we have introduced a Siamese network architecture integrated with convolutional neural networks (CNNs) for spatial feature extraction, bidirectional LSTM (Bi-LSTM) for temporal dependency capture, and attention mechanisms to prioritize salient features. Employing both contrastive and triplet loss functions, we meticulously analyze the impact of these learning approaches on the network’s ability to generate discriminative embeddings for HAR tasks. Through extensive experimentation, the study reveals that Siamese networks, particularly those utilizing triplet loss functions, demonstrate superior performance in activity recognition accuracy and F1 scores compared with baseline deep learning models. The inclusion of a stacking meta-classifier further amplifies classification efficacy, showcasing the robustness and adaptability of our proposed model. Conclusively, our findings underscore the potential of Siamese networks with advanced learning paradigms in enhancing HAR systems, paving the way for future research in model optimization and application expansion.
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Aharchaou, Mehdi, Michael Matheney, Joe Molyneux, and Erik Neumann. "Edge-aware filtering with Siamese neural networks." Leading Edge 39, no. 10 (October 2020): 711–17. http://dx.doi.org/10.1190/tle39100711.1.
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Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.
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Gao, Peng, Xiao Liu, Hong-Chuan Sang, Yu Wang, and Fei Wang. "Efficient and Lightweight Visual Tracking with Differentiable Neural Architecture Search." Electronics 12, no. 17 (August 27, 2023): 3623. http://dx.doi.org/10.3390/electronics12173623.
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Over the last decade, Siamese network architectures have emerged as dominating tracking paradigms, which have led to significant progress. These architectures are made up of a backbone network and a head network. The backbone network comprises two identical feature extraction sub-branches, one for the target template and one for the search candidate. The head network takes both the template and candidate features as inputs and produces a local similarity score for the target object in each location of the search candidate. Despite promising results that have been attained in visual tracking, challenges persist in developing efficient and lightweight models due to the inherent complexity of the task. Specifically, manually designed tracking models that rely heavily on the knowledge and experience of relevant experts are lacking. In addition, the existing tracking approaches achieve excellent performance at the cost of large numbers of parameters and vast amounts of computations. A novel Siamese tracking approach called TrackNAS based on neural architecture search is proposed to reduce the complexity of the neural architecture applied in visual tracking. First, according to the principle of the Siamese network, backbone and head network search spaces are constructed, constituting the search space for the network architecture. Next, under the given resource constraints, the network architecture that meets the tracking performance requirements is obtained by optimizing a hybrid search strategy that combines distributed and joint approaches. Then, an evolutionary method is used to lighten the network architecture obtained from the search phase to facilitate deployment to devices with resource constraints (FLOPs). Finally, to verify the performance of TrackNAS, comparison and ablation experiments are conducted using several large-scale visual tracking benchmark datasets, such as OTB100, VOT2018, UAV123, LaSOT, and GOT-10k. The results indicate that the proposed TrackNAS achieves competitive performance in terms of accuracy and robustness, and the number of network parameters and computation volume are far smaller than those of other advanced Siamese trackers, meeting the requirements for lightweight deployment to resource-constrained devices.
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Melnychenko, Artem, and Kostyantyn Zdor. "EFFICIENCY OF SUPPLEMENTARY OUTPUTS IN SIAMESE NEURAL NETWORKS." Advanced Information Systems 7, no. 3 (September 20, 2023): 49–53. http://dx.doi.org/10.20998/2522-9052.2023.3.07.
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In the world of image analysis, effectively handling large image datasets is a complex challenge that requires using deep neural networks. Siamese neural networks, known for their twin-like structure, offer an effective solution to image comparison tasks, especially when data volume is limited. This research explores the possibility of enhancing these models by adding supplementary outputs that improve classification and help find specific data features. The article shows the results of two experiments using the Fashion MNIST and PlantVillage datasets, incorporating additional classification, regression, and combined output strategies with various weight loss configurations. The results from the experiments show that for simpler datasets, the introduction of supplementary outputs leads to a decrease in model accuracy. Conversely, for more complex datasets, optimal accuracy was achieved through the simultaneous integration of regression and classification supplementary outputs. It should be noted that the observed increase in accuracy is relatively marginal and does not guarantee a substantial impact on the overall accuracy of the model.
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Contreras, Jhonatan, Sara Mostafapour, Jürgen Popp, and Thomas Bocklitz. "Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy." Molecules 29, no. 5 (February 28, 2024): 1061. http://dx.doi.org/10.3390/molecules29051061.
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Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.
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Bilotserkovskyy, V. V., S. G. Udovenko, and L. E. Chala. "Method of neural network recognition of falsified images." Bionics of Intelligence 2, no. 95 (December 2, 2020): 32–42. http://dx.doi.org/10.30837/bi.2020.2(95).05.
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Methods for generating images falsified using Deepfake technologies and methods for detecting them are considered. A method for detecting falsified images is proposed, based on the joint use of an ensemble of convolutional neural models, the Attention mechanism and a Siamese network learning strategy. The ensembles of models were formed in different ways (using two, three or more components). The result was calculated as the average value of the AUC and LogLoss indices from all the models included in the ensemble. This approach improves the accuracy of convolutional neural network classifiers for detecting static and dynamic images created using Deepfake technologies.
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Xie, Chunli, Xia Wang, Cheng Qian, and Mengqi Wang. "A Source Code Similarity Based on Siamese Neural Network." Applied Sciences 10, no. 21 (October 26, 2020): 7519. http://dx.doi.org/10.3390/app10217519.
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Finding similar code snippets is a fundamental task in the field of software engineering. Several approaches have been proposed for this task by using statistical language model which focuses on syntax and structure of codes rather than deep semantic information underlying codes. In this paper, a Siamese Neural Network is proposed that maps codes into continuous space vectors and try to capture their semantic meaning. Firstly, an unsupervised pre-trained method that models code snippets as a weighted series of word vectors. The weights of the series are fitted by the Term Frequency-Inverse Document Frequency (TF-IDF). Then, a Siamese Neural Network trained model is constructed to learn semantic vector representation of code snippets. Finally, the cosine similarity is provided to measure the similarity score between pairs of code snippets. Moreover, we have implemented our approach on a dataset of functionally similar code. The experimental results show that our method improves some performance over single word embedding method.
Dissertations / Theses on the topic "Siamese Neural Models"
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Gu, Jian. "Multi-modal Neural Representations for Semantic Code Search." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279101.
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In recent decades, various software systems have gradually become the basis of our society. Programmers search existing code snippets from time to time in their daily life. It would be beneficial and meaningful to have better solutions for the task of semantic code search, which is to find the most semantically relevant code snippets for a given query. Our approach is to introduce tree representations by multi-modal learning. The core idea is to enrich semantic information for code snippets by preparing data of different modalities, and meanwhile ignore syntactic information. We design one novel tree structure named Simplified Semantic Tree and then extract RootPath representations from that. We utilize RootPath representation to complement the conventional sequential representation, namely the token sequence of the code snippet. Our multi-modal model receives code-query pair as input and computes similarity score as output, following the pseudo-siamese architecture. For each pair, besides the ready-made code sequence and query sequence, we extra one extra tree sequence from Simplified Semantic Tree. There are three encoders in our model, and they respectively encode these three sequences as vectors of the same length. Then we combine the code vector with the tree vector for one joint vector, which is still of the same length, as the multi-modal representation for the code snippet. We introduce triplet loss to ensure vectors of code and query in the same pair be close at the shared vector space. We conduct experiments in one large-scale multi-language corpus, with comparisons of strong baseline models by specified performance metrics. Among baseline models, the simplest Neural Bag-of-Words model is with the most satisfying performance. It indicates that syntactic information is likely to distract complex models from critical semantic information. Results show that our multi-modal representation approach performs better because it surpasses baseline models by far in most cases. The key to our multi-modal model is that it is totally about semantic information, and it learns from data of multiple modalities.Under de senaste decennierna har olika programvarusystem gradvis blivit basen i vårt samhälle. Programmerare söker i befintliga kodavsnitt från tid till annan i deras dagliga liv. Det skulle vara fördelaktigt och meningsfullt att ha bättre lösningar för uppgiften att semantisk kodsökning, vilket är att hitta de mest semantiskt relevanta kodavsnitten för en given fråga. Vår metod är att introducera trädrepresentationer genom multimodal inlärning. Grundidén är att berika semantisk information för kodavsnitt genom att förbereda data med olika modaliteter och samtidigt ignorera syntaktisk information. Vi designar en ny trädstruktur med namnet Simplified Semantic Tree och extraherar sedan RootPath-representationer från det. Vi använder RootPath-representation för att komplettera den konventionella sekvensrepresentationen, nämligen kodsekvensens symbolsekvens. Vår multimodala modell får kodfrågeställningar som inmatning och beräknar likhetspoäng som utgång efter den pseudo-siamesiska arkitekturen. För varje par, förutom den färdiga kodsekvensen och frågesekvensen, extrager vi en extra trädsekvens från Simplified Semantic Tree. Det finns tre kodare i vår modell, och de kodar respektive tre sekvenser som vektorer av samma längd. Sedan kombinerar vi kodvektorn med trädvektorn för en gemensam vektor, som fortfarande är av samma längd som den multimodala representationen för kodavsnittet. Vi introducerar tripletförlust för att säkerställa att vektorer av kod och fråga i samma par är nära det delade vektorn. Vi genomför experiment i ett storskaligt flerspråkigt korpus, med jämförelser av starka baslinjemodeller med specificerade prestandametriker. Bland baslinjemodellerna är den enklaste Neural Bag-of-Words-modellen med den mest tillfredsställande prestanda. Det indikerar att syntaktisk information sannolikt kommer att distrahera komplexa modeller från kritisk semantisk information. Resultaten visar att vår multimodala representationsmetod fungerar bättre eftersom den överträffar basmodellerna i de flesta fall. Nyckeln till vår multimodala modell är att den helt handlar om semantisk information, och den lär sig av data om flera modaliteter.
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Menad, Safaa. "Enrichissement et alignement sémantique d'οntοlοgies biοmédicales par mοdèles de langue." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR104.
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La première partie de cette thèse traite de la conception de modèles neuronaux siamois entraînés pour la similarité sémantique entre textes biomédicaux et de leur application à des tâches de TAL sur des documents biomédicaux. L’entraînement de ces modèles a été réalisé en plongeant les titres et résumés du corpus PubMed avec le thésaurus MeSH dans un même espace de représentation. Dans la seconde partie nous utilisons ces modèles pour aligner et enrichir les terminologies de l’UMLS (Unified Medical Language System) et automatiser l’intégration de nouvelles relations entre concepts similaires provenant notamment de maladies (DOID), de médicaments (DRON) et de symptômes. Ces relations enrichies permettent d’améliorer l’exploitation de ces ontologies, facilitant ainsi leur utilisation dans diverses applications cliniques et scientifiques. Nous proposons de plus des approches de validation à l’aide des ressources telles que les LLMs, l’OpenFDA, le Métathésaurus et le réseau sémantique de l’UMLS que nous complétons par la validation manuelle d’experts du domaineThe first part of this thesis addresses the design of siamese neural models trained for semantic similarity between biomedical texts and their application to NLP tasks on biomedical documents. The training of these models was performed by embedding the titles and abstracts from the PubMed corpus along with the MeSH thesaurus into a common space. In the second part, we use these models to align and enrich the terminologies of UMLS (Unified Medical Language System) and automate the integration of new relationships between similar concepts, particularly from diseases (DOID), drugs (DRON), and symptoms. These enriched relationships enhance the usability of these ontologies, thereby facilitating their application in various clinical and scientific domains. Additionally, we propose validation approaches using resources such as LLMs, OpenFDA, the UMLS Metathesaurus, and the UMLS semantic network, supplemented by manual validation from domain experts
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Gong, Rong. "Automatic assessment of singing voice pronunciation: a case study with Jingju music." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/664421.
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Online learning has altered music education remarkable in the last decade. Large and increasing amount of music performing learners participate in online music learning courses due to the easy-accessibility and boundless of time-space constraints.
Singing can be considered the most basic form of music performing. Automatic singing voice assessment, as an important task in Music Information Retrieval (MIR), aims to extract musically meaningful information and measure the quality of learners' singing voice.
Singing correctness and quality is culture-specific and its assessment requires culture-aware methodologies. Jingju (also known as Beijing opera) music is one of the representative music traditions in China and has spread to many places in the world where there are Chinese communities. Our goal is to tackle unexplored automatic singing voice pronunciation assessment problems in jingju music, to make the current eurogeneric assessment approaches more culture-aware, and in return, to develop new assessment approaches which can be generalized to other musical traditions.El aprendizaje en línea ha cambiado notablemente la educación musical en la pasada década. Una cada vez mayor cantidad de estudiantes de interpretación musical participan en cursos de aprendizaje musical en línea por su fácil accesibilidad y no estar limitada por restricciones de tiempo y espacio. Puede considerarse el canto como la forma más básica de interpretación. La evaluación automática de la voz cantada, como tarea importante en la disciplina de Recuperación de Información Musical (MIR por sus siglas en inglés) tiene como objetivo la extracción de información musicalmente significativa y la medición de la calidad de la voz cantada del estudiante. La corrección y calidad del canto son específicas a cada cultura y su evaluación requiere metodologías con especificidad cultural. La música del jingju (también conocido como ópera de Beijing) es una de las tradiciones musicales más representativas de China y se ha difundido a muchos lugares del mundo donde existen comunidades chinas.Nuestro objetivo es abordar problemas aún no explorados sobre la evaluación automática de la voz cantada en la música del jingju, hacer que las propuestas eurogenéticas actuales sobre evaluación sean más específicas culturalmente, y al mismo tiempo, desarrollar nuevas propuestas sobre evaluación que puedan ser generalizables para otras tradiciones musicales.
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Štarha, Dominik. "Meření podobnosti obrazů s pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377018.
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This master´s thesis deals with the reseach of technologies using deep learning method, being able to use when processing image data. Specific focus of the work is to evaluate the suitability and effectiveness of deep learning when comparing two image input data. The first – theoretical – part consists of the introduction to neural networks and deep learning. Also, it contains a description of available methods, their benefits and principles, used for processing image data. The second - practical - part of the thesis contains a proposal a appropriate model of Siamese networks to solve the problem of comparing two input image data and evaluating their similarity. The output of this work is an evaluation of several possible model configurations and highlighting the best-performing model parameters.
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Torti, López Rubén Ezequiel. "Entrenamiento de modelos de aprendizaje profundo mediante autosupervisión." Bachelor's thesis, 2017. http://hdl.handle.net/11086/6082.
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Tesis (Lic. en Ciencias de la Computación)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2017.Dentro del campo del aprendizaje automático, una clase de técnicas conocidas como Deep Learning (DL) han cobrado particular relevancia, ya que mediante su utilización se han conseguido mejoras muy significativas respecto de métodos tradicionales. Una desventaja de los modelos basados en DL es que usualmente cuentan con más parámetros que cantidad de elementos en los conjuntos de datos de entrenamiento. En el caso particular de la clasificación de imágenes por contenido, si bien existen grandes conjuntos de datos anotados disponibles, su generación para problemas en otros dominios es muy costosa. Se propone en este trabajo una manera alternativa al entrenamiento de esta clase de modelos inspirada en cómo los organismos vivientes desarrollan habilidades de percepción visual: moviéndose e interactuando con el mundo que los rodea. Partiendo de la hipótesis de que un agente puede usar la información del movimiento propio (rotación y traslación en los ejes X,Y,Z) como método de supervisión, Agrawal et al. ya han demostrado que es posible obtener buenos resultados entrenando con menos imágenes anotadas que lo usual. Se validan experimentalmente los resultados de este método de entrenamiento con respecto a los del estado del arte en tareas de clasificación en distintos dominios.
Within the field of machine learning, a class of techniques known as Deep Learning (DL) have become particularly relevant since their use have achieved significant improvements compared to traditional methods. A disadvantage of DL-based models is that they usually have much more parameters than elements in the training datasets. Despite the fact that there exist large annotated datasets for the task of image classification by content, the generation of new datasets for problems in other domains is very costly. There is an alternative way to train this kind of models inspired by how the living organisms develop visual perception skills: by moving and interacting with the world that surrounds them. By hypothesizing that an agent can use its own movement information (rotation and translation in the X, Y, Z axes) as a method of supervision, Agrawal et al. have already shown that it is possible to obtain good results when training with fewer annotated images than usual. In this work, the results of this method are validated with respect to the state of the art algorithms in tasks of classification in different domains.
Book chapters on the topic "Siamese Neural Models"
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Huang, Junrong, and Chenwei Wang. "VFIQ: A Novel Model of ViT-FSIMc Hybrid Siamese Network for Image Quality Assessment." In Neural Information Processing, 162–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8073-4_13.
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Li, Guangshuo, Lingling Cui, Yue Song, Xiaoxia Chen, and Lingxiao Zheng. "Small-Sample Coal-Rock Recognition Model Based on MFSC and Siamese Neural Network." In Green, Pervasive, and Cloud Computing, 238–47. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9893-7_18.
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Hu, Haiyan, Chang Su, and Jiaqi Ju. "Identification of Pests and Diseases Based on Cascaded Convolutional Neural Network." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde231208.
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Corn is one of the main food crops in China, and its diseases and pests directly affect China’s food security. In order to carry out real-time and accurate detection and recognition of corn pests and diseases in complex agricultural environments, this paper proposes a corn pests and diseases recognition method based on cascaded convolutional neural networks. Due to the differences in different deep learning models, two different convolutional neural backbone networks, AlexNet and Inception, were used to construct a double voting network model for corn common pests and diseases image classification, and accurate corn pests and diseases categories were calculated. Then, the siamese network model is used to calculate and measure the Euclidean distance between different degrees of pests and diseases, and the corresponding degree of corn pests and diseases is given. Finally, the output results of the fusion of the double voting network and the siamese network were used to calculate the categories and severity of corn pests and diseases in complex environments. Through a large number of experiments, it has been shown that the proposed method can effectively improve the accuracy of corn pest identification and pest severity assessment.
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Svensson, Karl Fabian, and Carlos Guerrero-Mosquera. "OPNet: A One-Shot Image Similarity Algorithm for Production Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230665.
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The appearance of new trends in the field of cognitive neuroscience, for example object persistence, has paved the way for the evolution of deep CNNs into Siamese Neural Network architectures such as OPNet. These networks allow for image recognition without the need for expensive labelled data. In this work, we apply this technology to a small Spanish tech e-commerce struggling with the production of their customizable products. Our goal was to automatically identify each product’s order in the company’s internal system by matching photos of the products taken by workers with system-generated images. After testing various architectures, we achieved 91% accuracy with a triplet loss model using deep CNN embedding networks. The algorithm was trained on a dataset of 9696 unique product images captured in the company’s production department. The paper details the technical aspects of the Siamese Neural Network architecture, including the triplet loss and SoftMax distance function used to train it. Our results demonstrate the potential of these deep learning models to generate practical benefits for firms, since it reduces human errors, while improving the effectiveness and efficiency of the company’s internal processes.
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Menad, Safaa, Saïd Abdeddaïm, and Lina F. Soualmia. "Merging Biomedical Ontologies with BioSTransformers." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240526.
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Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is crucial for structuring, coding, and retrieving data. However, available ontologies do not encompass all the relevant concepts and relationships. In this paper, we propose the framework SiMHOMer (Siamese Models for Health Ontologies Merging) to semantically merge and integrate the most relevant ontologies in the healthcare domain, with a first focus on diseases, symptoms, drugs, and adverse events. We propose to rely on the siamese neural models we developed and trained on biomedical data, BioSTransformers, to identify new relevant relations between concepts and to create new semantic relations, the objective being to build a new merging ontology that could be used in applications. To validate the proposed approach and the new relations, we relied on the UMLS Metathesaurus and the Semantic Network. Our first results show promising improvements for future research.
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Trappey, Amy J. C., Charles V. Trappey, and Sam C. C. Lin. "Detecting Trademark Image Infringement Using Convolutional Neural Networks." In Advances in Transdisciplinary Engineering. IOS Press, 2019. http://dx.doi.org/10.3233/atde190155.
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The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater corporate diligence to protect intangible assets such as brands which can easily be coped or placed in grey markets. Trademarks are the government registered legal intellectual property rights (IPRs) used to protect companies’ brands and build brand equity. Given the rapid growth in the number of global trademark registrations, the number of trademark infringement cases is also increasing, a great challenge for the original trademark owner to detect the infringement and takes action to protect the brand image and related commercial interests. This research develops a trademark similarity assessment methodology based on the US trademark law related to the high likelihood of confusion and associated regulations. The research focuses on identifying trade mark image similarity using a deep learning approach. The convolutional neural network (CNN) and Siamese neural network (SNN) algorithms are modeled and trained using Cifar-10 and TopLogo-10 corpuses. These corpuses consist of more than 100,000 positive image pairs and more than 150,000 negative image pairs as training data. After training the model, an image input to the model extracts and recommends similar trade mark images found in the corpus. The solution assists users registering new trademarks to identifying similar marks that may lead to disputes. The solution also automatically screens images to identify marks that potentially infringe upon registered trademarks.
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Fonseca, Nayara, and Veronica Guidetti. "Generalizing Similarity in Noisy Setups: The DIBS Phenomenon." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230339.
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This work uncovers an interplay among data density, noise, and the generalization ability in similarity learning. We consider Siamese Neural Networks (SNNs), which are the basic form of contrastive learning, and explore two types of noise that can impact SNNs, Pair Label Noise (PLN) and Single Label Noise (SLN). Our investigation reveals that SNNs exhibit double descent behaviour regardless of the training setup and that it is further exacerbated by noise. We demonstrate that the density of data pairs is crucial for generalization. When SNNs are trained on sparse datasets with the same amount of PLN or SLN, they exhibit comparable generalization properties. However, when using dense datasets, PLN cases generalize worse than SLN ones in the overparametrized region, leading to a phenomenon we call Density-Induced Break of Similarity (DIBS). In this regime, PLN similarity violation becomes macroscopical, corrupting the dataset to the point where complete interpolation cannot be achieved, regardless of the number of model parameters. Our analysis also delves into the correspondence between online optimization and offline generalization in similarity learning. The results show that this equivalence fails in the presence of label noise in all the scenarios considered.
Conference papers on the topic "Siamese Neural Models"
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Liang, Jianqiang, Yuanhang Zhu, Hu Long, Shulan Jiang, Tielin Shi, and Jian Duan. "An Intelligent Tool Wear Prediction Model Based on an Improved Regressive Siamese Neural Network with Various Sample Sizes." In 2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing), 1–7. IEEE, 2024. https://doi.org/10.1109/phm-beijing63284.2024.10874460.
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Park, Donghyeon, Keonwoo Kim, Yonggyu Park, Jungwoon Shin, and Jaewoo Kang. "KitcheNette: Predicting and Ranking Food Ingredient Pairings using Siamese Neural Network." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/822.
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As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers. In this work, we propose KitcheNette which is a model that predicts food ingredient pairing scores and recommends optimal ingredient pairings. KitcheNette employs Siamese neural networks and is trained on our annotated dataset containing 300K scores of pairings generated from numerous ingredients in food recipes. As the results demonstrate, our model not only outperforms other baseline models, but also can recommend complementary food pairings and discover novel ingredient pairings.
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Aliane, Ahmed Amine, and Hassina Aliane. "Evaluating SIAMESE Architecture Neural Models for Arabic Textual Similarity and Plagiarism Detection." In 2020 4th International Symposium on Informatics and its Applications (ISIA). IEEE, 2020. http://dx.doi.org/10.1109/isia51297.2020.9416550.
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Trad, Fouad, Ali Hussein, and Ali Chehab. "Assessing the Effectiveness of Siamese Neural Networks to Mitigate Frequent Retraining in IoT Device Identification Models." In 2023 International Conference on Platform Technology and Service (PlatCon). IEEE, 2023. http://dx.doi.org/10.1109/platcon60102.2023.10255208.
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Nagaraj, Guru, Prashanth Pillai, and Mandar Kulkarni. "Deep Similarity Learning for Well Test Model Identification." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204675-ms.
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Abstract Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction. Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses. For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates. Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis. This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction. This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.
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Huang, Shuting, Zefeng Liu, and Zhenyu Liu. "Multimodal Siamese Model for Breast Cancer Survival Prediction." In 2024 4th International Conference on Neural Networks, Information and Communication (NNICE). IEEE, 2024. http://dx.doi.org/10.1109/nnice61279.2024.10498910.
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Sivkova, Tatyana, Aleksandr Gusev, and Artem Syropyatov. "Technology for Cast Iron Microstructure Analysis in SIAMS Software Using Neural Networks." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-772-780.
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The paper covers key issues of metal and alloys’ microstructure control using cast iron microstructure examples, and ways of resolving these issues by integration of neural networks into algorithms of SIAMS software. Paper lists key specifics of using the technology and training neural network, aimed at improving algorithm reproducibility, analysis acceleration and simplification. The method for training neural network models as part of the SIAMS software includes functionality for assessing the quality of training. The described method allows you control the model error using the value of the loss function. Developed algorithms in form of ready solutions were integrated into the SIAMS software package, and can be recommended for serial microstructure control in industrial laboratories.
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Liu, Yuxing, Geng Chang, Guofeng Fu, Yingchao Wei, Jie Lan, and Jiarui Liu. "Self-Attention based Siamese Neural Network recognition Model." In 2022 34th Chinese Control and Decision Conference (CCDC). IEEE, 2022. http://dx.doi.org/10.1109/ccdc55256.2022.10034228.
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Zhou, Xinxin, Zhaohui Zhang, Lizhi Wang, and Pengwei Wang. "A Model Based on Siamese Neural Network for Online Transaction Fraud Detection." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852295.
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Chen, Donghang, Xiang Zhang, Ran Tian, Yingqi Tang, Yujie Hu, and Shaozhi Wu. "Online Multi-Object Tracking with United Siamese Network and Candidate-Refreshing Model." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533479.
Full textReports on the topic "Siamese Neural Models"
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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), October 2024. http://dx.doi.org/10.21079/11681/49442.
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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the internal twinned model, ResNet50 and MobileNetV2, with the former historically having higher accuracy and the latter historically having faster processing time. When trained and tested on experimentally obtained GPR profiles with synthetically added changes, ResNet50 had a higher accuracy. Thanks to this higher accuracy, less computational processing was needed, leading to ResNet50 needing only 107 s to make a prediction compared to MobileNetV2 needing 223 s. Results imply that twinned models with higher historical accuracies should be investigated further. It is also recommended to test Siamese Twin models further with experimentally produced changes to verify the changed detection model’s accuracy is not merely specific to synthetically produced changes.