Relevant bibliographies by topics / Zero-shot generalization

Academic literature on the topic 'Zero-shot generalization'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Zero-shot generalization"

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Chang, Shuaichen, Pengfei Liu, Yun Tang, Jing Huang, Xiaodong He, and Bowen Zhou. "Zero-Shot Text-to-SQL Learning with Auxiliary Task." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7488–95. http://dx.doi.org/10.1609/aaai.v34i05.6246.

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Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task. However, little work has paid attention to how these models generalize to realistic unseen data, which naturally raises a question: does this impressive performance signify a perfect generalization model, or are there still some limitations?In this paper, we first diagnose the bottleneck of the text-to-SQL task by providing a new testbed, in which we observe that existing models present poor generalization ability on rarely-seen data. The above analysis encourages us to design a simple but effective auxiliary task, which serves as a supportive model as well as a regularization term to the generation task to increase the models' generalization. Experimentally, We evaluate our models on a large text-to-SQL dataset WikiSQL. Compared to a strong baseline coarse-to-fine model, our models improve over the baseline by more than 3% absolute in accuracy on the whole dataset. More interestingly, on a zero-shot subset test of WikiSQL, our models achieve 5% absolute accuracy gain over the baseline, clearly demonstrating its superior generalizability.
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Pan, Chongyu, Jian Huang, Jianguo Hao, and Jianxing Gong. "Towards zero-shot learning generalization via a cosine distance loss." Neurocomputing 381 (March 2020): 167–76. http://dx.doi.org/10.1016/j.neucom.2019.11.011.

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Lee, Harrison, Raghav Gupta, Abhinav Rastogi, Yuan Cao, Bin Zhang, and Yonghui Wu. "SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10938–46. http://dx.doi.org/10.1609/aaai.v36i10.21341.

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Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research. The Schema-Guided Dialogue (SGD) dataset introduced a paradigm for enabling models to support any service in zero-shot through schemas, which describe service APIs to models in natural language. We explore the robustness of dialogue systems to linguistic variations in schemas by designing SGD-X - a benchmark extending SGD with semantically similar yet stylistically diverse variants for every schema. We observe that two top state tracking models fail to generalize well across schema variants, measured by joint goal accuracy and a novel metric for measuring schema sensitivity. Additionally, we present a simple model-agnostic data augmentation method to improve schema robustness.
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Deutsch, Shay, Andrea Bertozzi, and Stefano Soatto. "Zero Shot Learning with the Isoperimetric Loss." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10704–12. http://dx.doi.org/10.1609/aaai.v34i07.6698.

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We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting. We use a pre-trained deep neural network model as a visual representation of image data, a Word2Vec embedding of class labels, and linear maps between the visual and semantic embedding spaces. However, the spaces themselves are not linear, and we postulate the sample embedding to be populated by noisy samples near otherwise smooth manifolds. We exploit the graph structure defined by the sample points to regularize the estimates of the manifolds by inferring the graph connectivity using a generalization of the isoperimetric inequalities from Riemannian geometry to graphs. Surprisingly, this regularization alone, paired with the simplest baseline model, outperforms the state-of-the-art among fully automated methods in zero-shot learning benchmarks such as AwA and CUB. This improvement is achieved solely by learning the structure of the underlying spaces by imposing regularity.
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Chen, Yongrui, Xinnan Guo, Chaojie Wang, Jian Qiu, Guilin Qi, Meng Wang, and Huiying Li. "Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 3992–4000. http://dx.doi.org/10.1609/aaai.v35i5.16519.

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Single-table text-to-SQL aims to transform a natural language question into a SQL query according to one single table. Recent work has made promising progress on this task by pre-trained language models and a multi-submodule framework. However, zero-shot table, that is, the invisible table in the training set, is currently the most critical bottleneck restricting the application of existing approaches to real-world scenarios. Although some work has utilized auxiliary tasks to help handle zero-shot tables, expensive extra manual annotation limits their practicality. In this paper, we propose a new approach for the zero-shot text-to-SQL task which does not rely on any additional manual annotations. Our approach consists of two parts. First, we propose a new model that leverages the abundant information of table content to help establish the mapping between questions and zero-shot tables. Further, we propose a simple but efficient meta-learning strategy to train our model. The strategy utilizes the two-step gradient update to force the model to learn a generalization ability towards zero-shot tables. We conduct extensive experiments on a public open-domain text-to-SQL dataset WikiSQL and a domain-specific dataset ESQL. Compared to existing approaches using the same pre-trained model, our approach achieves significant improvements on both datasets. Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive. More importantly, on the zero-shot subsets of both the datasets, our approach further increases the improvements.
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Sherborne, Tom, and Mirella Lapata. "Meta-Learning a Cross-lingual Manifold for Semantic Parsing." Transactions of the Association for Computational Linguistics 11 (2023): 49–67. http://dx.doi.org/10.1162/tacl_a_00533.

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Abstract Localizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer. Our algorithm uses high-resource languages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling ≤10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling ≤10% of training data.1
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Chen, Binghui, and Weihong Deng. "Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8134–41. http://dx.doi.org/10.1609/aaai.v33i01.33018134.

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Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in zeroshot image retrieval and clustering (ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this ’good’ embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this ’good’ embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning (ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to ’confuse’ the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to ’confuse’ the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-theart performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. Code available at http://www.bhchen.cn/.
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Zhang, Zhaolong, Yuejie Zhang, Rui Feng, Tao Zhang, and Weiguo Fan. "Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12943–50. http://dx.doi.org/10.1609/aaai.v34i07.6993.

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Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can effectively narrow the domain gap and transfer the knowledge. Furthermore, we generate the semantic information from the visual information using a Conditional Variational Autoencoder rather than only map them back from the visual space to the semantic space, which enhances the generalization ability of our model. Besides, feature loss, classification loss, and semantic loss are introduced to optimize our proposed SketchGCN model. Our model gets a good performance on the challenging Sketchy and TU-Berlin datasets.
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Wang, Yu. "Bi-shifting semantic auto-encoder for zero-shot learning." Electronic Research Archive 30, no. 1 (2021): 140–67. http://dx.doi.org/10.3934/era.2022008.

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<abstract><p>Zero-shot learning aims to transfer the model of labeled seen classes in the source domain to the disjoint unseen classes without annotations in the target domain. Most existing approaches generally consider directly adopting the visual-semantic projection function learned in the source domain to the target domain without adaptation. However, due to the distribution discrepancy between the two domains, it remains challenging in dealing with the projection domain shift problem. In this work, we formulate a novel bi-shifting semantic auto-encoder to learn the semantic representations of the target instances and reinforce the generalization ability of the projection function. The encoder aims at mapping the visual features into the semantic space by leveraging the visual features of target instances and is guided by the semantic prototypes of seen classes. While two decoders manage to respectively reconstruct the original visual features in the source and target domains. Thus, our model can capture the generalized semantic characteristics related with the seen and unseen classes to alleviate the projection function problem. Furthermore, we develop an efficient algorithm by the advantage of the linear projection functions. Extensive experiments on the five benchmark datasets demonstrate the competitive performance of our proposed model.</p></abstract>
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Liu, Lu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. "Attribute Propagation Network for Graph Zero-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4868–75. http://dx.doi.org/10.1609/aaai.v34i04.5923.

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The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the “attribute propagation network (APNet)”, which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.
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Conference papers on the topic "Zero-shot generalization"

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Tavares, Diogo. "Zero-shot Generalization of Multimodal Dialogue Agents." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548759.

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Ye, Chang, Ahmed Khalifa, Philip Bontrager, and Julian Togelius. "Rotation, Translation, and Cropping for Zero-Shot Generalization." In 2020 IEEE Conference on Games (CoG). IEEE, 2020. http://dx.doi.org/10.1109/cog47356.2020.9231907.

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Kim, Seungyeon, Yejin Park, and Junseong Bang. "Dialogue State Tracking with Zero-Shot and Few-Shot Learning for Generalization: A Review." In 2022 International Conference on Platform Technology and Service (PlatCon). IEEE, 2022. http://dx.doi.org/10.1109/platcon55845.2022.9932101.

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Li, Shuyang, Jin Cao, Mukund Sridhar, Henghui Zhu, Shang-Wen Li, Wael Hamza, and Julian McAuley. "Zero-shot Generalization in Dialog State Tracking through Generative Question Answering." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-main.91.

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Kumar, Visak, Sehoon Ha, and Karen Liu. "Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments." In Robotics: Science and Systems 2021. Robotics: Science and Systems Foundation, 2021. http://dx.doi.org/10.15607/rss.2021.xvii.065.

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Baradaran, Razieh, and Hossein Amirkhani. "Zero-Shot Estimation of Base Models’ Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization." In 2021 26th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2021. http://dx.doi.org/10.1109/csicc52343.2021.9420549.

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Konovalov, V. P., P. A. Gulyaev, A. A. Sorokin, Y. M. Kuratov, and M. S. Burtsev. "EXPLORING THE BERT CROSS-LINGUAL TRANSFER FOR READING COMPREHENSION." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-445-453.

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Multilingual BERT has been shown to generalize well in a zero-shot crosslingual setting. This generalization was measured on POS and NER tasks. We explore the multilingual BERT cross-language transferability on the reading comprehension task. We compare different modes of training of question-answering model for a non-English language using both English and language-specific data. We demonstrate that the model based on multilingual BERT is slightly behind the monolingual BERT-based on Russian data, however, it achieves comparable results with the language-specific variant on Chinese. We also show that training jointly on English data and additional 10,000 monolingual samples allows it to reach the performance comparable to the one trained on monolingual data only.
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Raina, Ayush, Jonathan Cagan, and Christopher McComb. "Self Learning Design Agent (SLDA): Enabling Deep Learning and Tree Search in Complex Action Spaces." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89740.

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Abstract Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases us towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network leverages self-generated experience to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data, and second, it illustrates a zero-shot generalization of generative strategies across various unseen boundary conditions. This work evaluates the effectiveness and versatility of the framework by solving multiple versions of the truss design problem without retraining. Overall, this paper presents a methodology to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space, circumventing the needs for expert data, existing solutions, and problem-specific learning.
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Liu, Yuhang, Wei Wei, Daowan Peng, and Feida Zhu. "Declaration-based Prompt Tuning for Visual Question Answering." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/453.

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In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e.g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e.g., VQA) via a brand-new objective function, e.g., answer prediction. However, the inconsistency of the objective forms not only severely limits the generalization of pre-trained VL models to downstream tasks, but also requires a large amount of labeled data for fine-tuning. To alleviate the problem, we propose an innovative VL fine-tuning paradigm (named Declaration-based Prompt Tuning, abbreviated as DPT), which fine-tunes the model for downstream VQA using the pre-training objectives, boosting the effective adaptation of pre-trained models to the downstream task. Specifically, DPT reformulates the VQA task via (1) textual adaptation, which converts the given questions into declarative sentence form for prompt-tuning, and (2) task adaptation, which optimizes the objective function of VQA problem in the manner of pre-training phase. Experimental results on GQA dataset show that DPT outperforms the fine-tuned counterpart by a large margin regarding accuracy in both fully-supervised (2.68%) and zero-shot/fewshot (over 31%) settings. All the data and codes will be available to facilitate future research.
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