# Variational Feature Disentangling for Fine-Grained Few-Shot Classification

arXiv: https://arxiv.org/abs/2010.03255 · HTML: https://mlsanity.com/paper/2010.03255/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 26 of 26)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Disentangled Representation Learning](https://mlsanity.com/paper/2211.11695/) (2022)
- [Zero-shot Object Counting](https://mlsanity.com/paper/2303.02001/) (2023)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [Few-Shot Object Detection via Variational Feature Aggregation](https://mlsanity.com/paper/2301.13411/) (2023)
- [Disentangled Feature Representation for Few-shot Image Classification](https://mlsanity.com/paper/2109.12548/) (2021)
- [Zero-Shot Object Counting with Language-Vision Models](https://mlsanity.com/paper/2309.13097/) (2023)
- [Assessing Sample Quality via the Latent Space of Generative Models](https://mlsanity.com/paper/2407.15171/) (2024)
- [Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition](https://mlsanity.com/paper/2305.07180/) (2023)
- [Few-shot Personalized Scanpath Prediction](https://mlsanity.com/paper/2504.05499/) (2025)
- [Disentangled Representation Learning with Transmitted Information Bottleneck](https://mlsanity.com/paper/2311.01686/) (2023)
- [Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation](https://mlsanity.com/paper/2407.12630/) (2024)
- [Learning Compositional Representations for Effective Low-Shot Generalization](https://mlsanity.com/paper/2204.08090/) (2022)
- [Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling](https://mlsanity.com/paper/2205.05155/) (2022)
- [Disentangled Generation with Information Bottleneck for Few-Shot Learning](https://mlsanity.com/paper/2211.16185/) (2022)
- [Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions](https://mlsanity.com/paper/2309.08097/) (2023)
- [CORA: Consistency-Guided Semi-Supervised Framework for Reasoning Segmentation](https://mlsanity.com/paper/2511.17755/) (2025)
- [CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound](https://mlsanity.com/paper/2206.08524/) (2022)
- [Fine-grained Few-shot Recognition by Deep Object Parsing](https://mlsanity.com/paper/2207.07110/) (2022)
- [Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning](https://mlsanity.com/paper/2502.01183/) (2025)
- [Learning Speaker-Invariant Visual Features for Lipreading](https://mlsanity.com/paper/2506.07572/) (2025)
- [Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2506.20263/) (2025)
- [Shallow Deep Learning Can Still Excel in Fine-Grained Few-Shot Learning](https://mlsanity.com/paper/2507.22041/) (2025)
- [MetaLab: Few-Shot Game Changer for Image Recognition](https://mlsanity.com/paper/2507.22057/) (2025)
- [Color as the Impetus: Transforming Few-Shot Learner](https://mlsanity.com/paper/2507.22136/) (2025)
- [Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning](https://mlsanity.com/paper/2512.06179/) (2025)

## Abstract (arXiv)

Fine-grained few-shot recognition often suffers from the problem of training data scarcity for novel categories.The network tends to overfit and does not generalize well to unseen classes due to insufficient training data. Many methods have been proposed to synthesize additional data to support the training. In this paper, we focus one enlarging the intra-class variance of the unseen class to improve few-shot classification performance. We assume that the distribution of intra-class variance generalizes across the base class and the novel class. Thus, the intra-class variance of the base set can be transferred to the novel set for feature augmentation. Specifically, we first model the distribution of intra-class variance on the base set via variational inference. Then the learned distribution is transferred to the novel set to generate additional features, which are used together with the original ones to train a classifier. Experimental results show a significant boost over the state-of-the-art methods on the challenging fine-grained few-shot image classification benchmarks.