# Task Aligned Generative Meta-learning for Zero-shot Learning

arXiv: https://arxiv.org/abs/2103.02185 · HTML: https://mlsanity.com/paper/2103.02185/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 53 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 30 of 35)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Feature Generating Networks for Zero-Shot Learning](https://mlsanity.com/paper/1712.00981/) (2017)
- [Generate To Adapt: Aligning Domains using Generative Adversarial Networks](https://mlsanity.com/paper/1704.01705/) (2017)
- [Learning Deep Representations of Fine-grained Visual Descriptions](https://mlsanity.com/paper/1605.05395/) (2016)
- [Learning a Deep Embedding Model for Zero-Shot Learning](https://mlsanity.com/paper/1611.05088/) (2016)
- [Semantic Autoencoder for Zero-Shot Learning](https://mlsanity.com/paper/1704.08345/) (2017)
- [Latent Embeddings for Zero-shot Classification](https://mlsanity.com/paper/1603.08895/) (2016)
- [Meta-Transfer Learning for Zero-Shot Super-Resolution](https://mlsanity.com/paper/2002.12213/) (2020)
- [Generalized Zero-Shot Learning via Synthesized Examples](https://mlsanity.com/paper/1712.03878/) (2017)
- [Synthesized Classifiers for Zero-Shot Learning](https://mlsanity.com/paper/1603.00550/) (2016)
- [A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts](https://mlsanity.com/paper/1712.01381/) (2017)
- [Multi-source Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/1910.12181/) (2019)
- [Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks](https://mlsanity.com/paper/1712.01928/) (2017)
- [Zero-Shot Cross-Lingual Transfer with Meta Learning](https://mlsanity.com/paper/2003.02739/) (2020)
- [A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders](https://mlsanity.com/paper/1709.00663/) (2017)
- [Generative Dual Adversarial Network for Generalized Zero-shot Learning](https://mlsanity.com/paper/1811.04857/) (2018)
- [Zero-Resource Translation with Multi-Lingual Neural Machine Translation](https://mlsanity.com/paper/1606.04164/) (2016)
- [Learning Meta Model for Zero- and Few-shot Face Anti-spoofing](https://mlsanity.com/paper/1904.12490/) (2019)
- [Zero-Shot Sketch-Image Hashing](https://mlsanity.com/paper/1803.02284/) (2018)
- [A Simple Exponential Family Framework for Zero-Shot Learning](https://mlsanity.com/paper/1707.08040/) (2017)
- [Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning](https://mlsanity.com/paper/1904.10056/) (2019)
- [TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning](https://mlsanity.com/paper/1904.05967/) (2019)
- [Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits](https://mlsanity.com/paper/2001.04362/) (2020)
- [Attribute Propagation Network for Graph Zero-shot Learning](https://mlsanity.com/paper/2009.11816/) (2020)
- [Zero-Shot Task Transfer](https://mlsanity.com/paper/1903.01092/) (2019)

## Built on by (most cited first, 19 of 19)

- [Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://mlsanity.com/paper/2305.05848/) (2023)
- [Multimodality in Meta-Learning: A Comprehensive Survey](https://mlsanity.com/paper/2109.13576/) (2021)
- [Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning](https://mlsanity.com/paper/2303.00404/) (2023)
- [Attribute-Modulated Generative Meta Learning for Zero-Shot Classification](https://mlsanity.com/paper/2104.10857/) (2021)
- [Learning to Generalize without Bias for Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2502.20158/) (2025)
- [Deconstructed Generation-Based Zero-Shot Model](https://mlsanity.com/paper/2204.11280/) (2022)
- [Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning](https://mlsanity.com/paper/2201.01961/) (2022)
- [Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis](https://mlsanity.com/paper/2205.03230/) (2022)
- [HardBoost: Boosting Zero-Shot Learning with Hard Classes](https://mlsanity.com/paper/2201.05479/) (2022)
- [Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus](https://mlsanity.com/paper/2205.03231/) (2022)
- [A Conditional Probability Framework for Compositional Zero-shot Learning](https://mlsanity.com/paper/2507.17377/) (2025)
- [Neural Variational Learning for Grounded Language Acquisition](https://mlsanity.com/paper/2107.14593/) (2021)
- [Fuzzy Logic Visual Network (FLVN): A neuro-symbolic approach for visual features matching](https://mlsanity.com/paper/2307.16019/) (2023)
- [Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2203.02648/) (2022)
- [Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis](https://mlsanity.com/paper/2311.02300/) (2023)
- [High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2404.04953/) (2024)
- [A Benchmark of Generative Methods for Zero-Shot Environmental Sound Classification](https://mlsanity.com/paper/2412.03771/) (2024)
- [Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation](https://mlsanity.com/paper/2506.16718/) (2025)
- [Few-Shot Inspired Generative Zero-Shot Learning](https://mlsanity.com/paper/2507.01026/) (2025)

## 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.