mlsanityData to 7 Oct 2026

Task Aligned Generative Meta-learning for Zero-shot Learning

arXiv 2103.02185 ↗First posted 3 Mar 2021cs.CV cs.LGcited by 19

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Abstract (arXiv)

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. However, such models may have a strong bias towards seen classes during training. Meta-learning has been introduced to mitigate the basis, but meta-ZSL methods are inapplicable when tasks used for training are sampled from diverse distributions. In this regard, we propose a novel Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ). TGMZ mitigates the potentially biased training and enables meta-ZSL to accommodate real-world datasets containing diverse distributions. TGMZ incorporates an attribute-conditioned task-wise distribution alignment network that projects tasks into a unified distribution to deliver an unbiased model. Our comparisons with state-of-the-art algorithms show the improvements of 2.1%, 3.0%, 2.5%, and 7.6% achieved by TGMZ on AWA1, AWA2, CUB, and aPY datasets, respectively. TGMZ also outperforms competitors by 3.6% in generalized zero-shot learning (GZSL) setting and 7.9% in our proposed fusion-ZSL setting.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 35 of its 53 references that are in the map. First posted on arXiv 3 Mar 2021.

Builds on

35 of 53 references.

References · 35

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  3. 2016Matching Networks for One Shot Learningcited by 2,246
  4. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  5. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  6. 2018A Survey of Unsupervised Deep Domain Adaptationcited by 361
  7. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  8. 2017Generate To Adapt: Aligning Domains using Generative Adversarial Networkscited by 183
  9. 2016Learning Deep Representations of Fine-grained Visual Descriptionscited by 182
  10. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  11. 2017Semantic Autoencoder for Zero-Shot Learningcited by 133
  12. 2016Latent Embeddings for Zero-shot Classificationcited by 129
23 more
  1. 2020Meta-Transfer Learning for Zero-Shot Super-Resolutioncited by 116
  2. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  3. 2016Synthesized Classifiers for Zero-Shot Learningcited by 113
  4. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
  5. 2019Multi-source Domain Adaptation for Semantic Segmentationcited by 74
  6. 2017Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networkscited by 66
  7. 2020Zero-Shot Cross-Lingual Transfer with Meta Learningcited by 66
  8. 2017A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoderscited by 64
  9. 2018Generative Dual Adversarial Network for Generalized Zero-shot Learningcited by 61
  10. 2016Zero-Resource Translation with Multi-Lingual Neural Machine Translationcited by 46
  11. 2019Learning Meta Model for Zero- and Few-shot Face Anti-spoofingcited by 44
  12. 2018Zero-Shot Sketch-Image Hashingcited by 43
  13. 2017A Simple Exponential Family Framework for Zero-Shot Learningcited by 38
  14. 2019Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learningcited by 38
  15. 2019TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learningcited by 32
  16. 2020Multi-Source Domain Adaptation for Text Classification via DistanceNet-Banditscited by 30
  17. 2020Attribute Propagation Network for Graph Zero-shot Learningcited by 29
  18. 2019Zero-Shot Task Transfercited by 26
  19. 2018Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentationcited by 21
  20. 2018Zero-Shot Kernel Learningcited by 11
  21. 2020Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosiscited by 11
  22. 2016How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributescited by 3
  23. 2020Spectrum-Guided Adversarial Disparity Learningcited by 3

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Citing papers per year: 2021: 3; 2022: 6; 2023: 4; 2024: 2; 2025: 4.

19 papers.

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  1. 2023Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendationcited by 14
  2. 2021Multimodality in Meta-Learning: A Comprehensive Surveycited by 11
  3. 2023Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learningcited by 11
  4. 2021Attribute-Modulated Generative Meta Learning for Zero-Shot Classificationcited by 8
  5. 2025Learning to Generalize without Bias for Open-Vocabulary Action Recognitioncited by 7
  6. 2022Deconstructed Generation-Based Zero-Shot Modelcited by 6
  7. 2022Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learningcited by 4
  8. 2022Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosiscited by 3
  9. 2022HardBoost: Boosting Zero-Shot Learning with Hard Classescited by 2
  10. 2022Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnituscited by 2
  11. 2025A Conditional Probability Framework for Compositional Zero-shot Learningcited by 2
  12. 2021Neural Variational Learning for Grounded Language Acquisitioncited by 1
7 more
  1. 2023Fuzzy Logic Visual Network (FLVN): A neuro-symbolic approach for visual features matchingcited by 1
  2. 2022Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learningcited by 0
  3. 2023Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosiscited by 0
  4. 2024High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learningcited by 0
  5. 2024A Benchmark of Generative Methods for Zero-Shot Environmental Sound Classificationcited by 0
  6. 2025Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generationcited by 0
  7. 2025Few-Shot Inspired Generative Zero-Shot Learningcited by 0