mlsanityData to 7 Oct 2026

Bi-directional Distribution Alignment for Transductive Zero-Shot Learning

arXiv 2303.08698 ↗First posted 15 Mar 2023cs.CV cs.AIcited by 7

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Abstract (arXiv)

It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match. Although transductive ZSL (TZSL) attempts to improve this by allowing the use of unlabelled examples from the unseen classes, there is still a high level of distribution shift. We propose a novel TZSL model (named as Bi-VAEGAN), which largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. The key proposal of the model design includes (1) a bi-directional distribution alignment, (2) a simple but effective L_2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation approach. In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings. Code could be found at https://github.com/Zhicaiwww/Bi-VAEGAN

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 36 of its 54 references that are in the map. First posted on arXiv 15 Mar 2023.

Builds on

36 of 54 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
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  11. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
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24 more
  1. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  2. 2013Zero-Shot Learning by Convex Combination of Semantic Embeddingscited by 135
  3. 2020Domain Adaptation with Conditional Distribution Matching and Generalized Label Shiftcited by 124
  4. 2020A Unified View of Label Shift Estimationcited by 119
  5. 2020Adversarial Feature Hallucination Networks for Few-Shot Learningcited by 110
  6. 2018Multi-modal Cycle-consistent Generalized Zero-Shot Learningcited by 87
  7. 2019Leveraging the Invariant Side of Generative Zero-Shot Learningcited by 83
  8. 2017Multi-Label Zero-Shot Learning with Structured Knowledge Graphscited by 72
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  11. 2022Siamese Contrastive Embedding Network for Compositional Zero-Shot Learningcited by 55
  12. 2015Transductive Multi-view Zero-Shot Learningcited by 53
  13. 2022MSDN: Mutually Semantic Distillation Network for Zero-Shot Learningcited by 48
  14. 2021OntoZSL: Ontology-enhanced Zero-shot Learningcited by 45
  15. 2019Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspectivecited by 42
  16. 2017A Simple Exponential Family Framework for Zero-Shot Learningcited by 38
  17. 2019Transductive Zero-Shot Learning with Visual Structure Constraintcited by 36
  18. 2016Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learningcited by 30
  19. 2019Creativity Inspired Zero-Shot Learningcited by 27
  20. 2018Progressive Ensemble Networks for Zero-Shot Recognitioncited by 23
  21. 2019Semantically Aligned Bias Reducing Zero Shot Learningcited by 20
  22. 2021Hardness Sampling for Self-Training Based Transductive Zero-Shot Learningcited by 12
  23. 2022Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesiscited by 11
  24. 2022Learning Semantic Ambiguities for Zero-Shot Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 2.

7 papers.

  1. 2024Enhance Image Classification via Inter-Class Image Mixup with Diffusion Modelcited by 34
  2. 2023Locate and Verify: A Two-Stream Network for Improved Deepfake Detectioncited by 26
  3. 2024Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correctingcited by 26
  4. 2024ZeroDiff: Solidified Visual-Semantic Correlation in Zero-Shot Learningcited by 10
  5. 2025Multi-Granularity Mutual Refinement Network for Zero-Shot Learningcited by 0
  6. 2026ZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learningcited by 0
  7. 2026Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detectioncited by 0