# Bi-directional Distribution Alignment for Transductive Zero-Shot Learning

arXiv: https://arxiv.org/abs/2303.08698 · HTML: https://mlsanity.com/paper/2303.08698/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 54 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [Learning Deep Representations of Fine-grained Visual Descriptions](https://mlsanity.com/paper/1605.05395/) (2016)
- [f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning](https://mlsanity.com/paper/1903.10132/) (2019)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [Learning a Deep Embedding Model for Zero-Shot Learning](https://mlsanity.com/paper/1611.05088/) (2016)
- [Zero-Shot Learning by Convex Combination of Semantic Embeddings](https://mlsanity.com/paper/1312.5650/) (2013)
- [Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift](https://mlsanity.com/paper/2003.04475/) (2020)
- [A Unified View of Label Shift Estimation](https://mlsanity.com/paper/2003.07554/) (2020)
- [Adversarial Feature Hallucination Networks for Few-Shot Learning](https://mlsanity.com/paper/2003.13193/) (2020)
- [Multi-modal Cycle-consistent Generalized Zero-Shot Learning](https://mlsanity.com/paper/1808.00136/) (2018)
- [Leveraging the Invariant Side of Generative Zero-Shot Learning](https://mlsanity.com/paper/1904.04092/) (2019)
- [Multi-Label Zero-Shot Learning with Structured Knowledge Graphs](https://mlsanity.com/paper/1711.06526/) (2017)
- [On Feature Normalization and Data Augmentation](https://mlsanity.com/paper/2002.11102/) (2020)
- [Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks](https://mlsanity.com/paper/1712.01928/) (2017)
- [Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2206.14475/) (2022)
- [Transductive Multi-view Zero-Shot Learning](https://mlsanity.com/paper/1501.04560/) (2015)
- [MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning](https://mlsanity.com/paper/2203.03137/) (2022)
- [OntoZSL: Ontology-enhanced Zero-shot Learning](https://mlsanity.com/paper/2102.07339/) (2021)
- [Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective](https://mlsanity.com/paper/1909.05995/) (2019)
- [A Simple Exponential Family Framework for Zero-Shot Learning](https://mlsanity.com/paper/1707.08040/) (2017)
- [Transductive Zero-Shot Learning with Visual Structure Constraint](https://mlsanity.com/paper/1901.01570/) (2019)
- [Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning](https://mlsanity.com/paper/1605.08151/) (2016)

## Built on by (most cited first, 7 of 7)

- [Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model](https://mlsanity.com/paper/2403.19600/) (2024)
- [Locate and Verify: A Two-Stream Network for Improved Deepfake Detection](https://mlsanity.com/paper/2309.11131/) (2023)
- [Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting](https://mlsanity.com/paper/2410.19294/) (2024)
- [ZeroDiff: Solidified Visual-Semantic Correlation in Zero-Shot Learning](https://mlsanity.com/paper/2406.02929/) (2024)
- [Multi-Granularity Mutual Refinement Network for Zero-Shot Learning](https://mlsanity.com/paper/2511.08163/) (2025)
- [ZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learning](https://mlsanity.com/paper/2602.12401/) (2026)
- [Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection](https://mlsanity.com/paper/2604.17477/) (2026)

## 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