# Few-Shot Learning with Adaptive Weight Masking in Conditional GANs

arXiv: https://arxiv.org/abs/2412.03105 · HTML: https://mlsanity.com/paper/2412.03105/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [RSMamba: Remote Sensing Image Classification with State Space Model](https://mlsanity.com/paper/2403.19654/) (2024)
- [NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space](https://mlsanity.com/paper/2309.14616/) (2023)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function](https://mlsanity.com/paper/2408.11839/) (2024)
- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [Robust Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1805.08657/) (2018)
- [Text classification optimization algorithm based on graph neural network](https://mlsanity.com/paper/2408.15257/) (2024)

## Built on by (most cited first, 4 of 4)

- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Object Detection for Medical Image Analysis: Insights from the RT-DETR Model](https://mlsanity.com/paper/2501.16469/) (2025)
- [Performance Review on LLM for solving leetcode problems](https://mlsanity.com/paper/2502.15770/) (2025)
- [Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models](https://mlsanity.com/paper/2501.14859/) (2025)

## Abstract (arXiv)

Deep learning has revolutionized various fields, yet its efficacy is hindered by overfitting and the requirement of extensive annotated data, particularly in few-shot learning scenarios where limited samples are available. This paper introduces a novel approach to few-shot learning by employing a Residual Weight Masking Conditional Generative Adversarial Network (RWM-CGAN) for data augmentation. The proposed model integrates residual units within the generator to enhance network depth and sample quality, coupled with a weight mask regularization technique in the discriminator to improve feature learning from small-sample categories. This method addresses the core issues of robustness and generalization in few-shot learning by providing a controlled and clear augmentation of the sample space. Extensive experiments demonstrate that RWM-CGAN not only expands the sample space effectively but also enriches the diversity and quality of generated samples, leading to significant improvements in detection and classification accuracy on public datasets. The paper contributes to the advancement of few-shot learning by offering a practical solution to the challenges posed by data scarcity and the need for rapid generalization to new tasks or categories.