Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches

• URL: http://arxiv.org/abs/2411.16567v1
• Date: Mon, 25 Nov 2024 16:51:11 GMT
• Title: Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
• Authors: Yinqiu Feng, Aoran Shen, Jiacheng Hu, Yingbin Liang, Shiru Wang, Junliang Du,
• Abstract summary: This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning. It aims to tackle the challenges posed by small-sample data in fields such as drug discovery, target recognition, and malicious traffic detection. Results confirm that the MhERGAN algorithm developed in this research is highly effective for few-shot learning.
• Score: 35.431340001608476
• License:
• Abstract: This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, target recognition, and malicious traffic detection-this study proposes a novel strategy that leverages Generative Adversarial Networks (GANs) and advanced optimization techniques to improve model performance with limited data. Specifically, the paper addresses the noise and bias issues introduced by data augmentation methods, contrasting them with model-based approaches, such as fine-tuning and metric learning, which rely heavily on related datasets. By combining Markov Chain Monte Carlo (MCMC) sampling and discriminative model ensemble strategies within a GAN framework, the proposed model adjusts generative and discriminative distributions to simulate a broader range of relevant data. Furthermore, it employs MHLoss and a reparameterized GAN ensemble to enhance stability and accelerate convergence, ultimately leading to improved classification performance on small-sample images and structured datasets. Results confirm that the MhERGAN algorithm developed in this research is highly effective for few-shot learning, offering a practical solution that bridges data scarcity with high-performing model adaptability and generalization.

Related papers

• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings [1.5703963908242198]
  This paper introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based and kernel-based distillation. To validate our innovative approach, we conducted experiments on publicly available multi-source prostate MRI data.
  arXiv  Detail & Related papers  (2024-04-03T13:35:51Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Personalized Federated Learning with Contextual Modulation and Meta-Learning [2.7716102039510564]
  Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources. We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization capabilities.
  arXiv  Detail & Related papers  (2023-12-23T08:18:22Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• A Guide for Practical Use of ADMG Causal Data Augmentation [0.0]
  Causal data augmentation strategies have been pointed out as a solution to handle these challenges. This paper experimentally analyzed the ADMG causal augmentation method considering different settings.
  arXiv  Detail & Related papers  (2023-04-03T09:31:13Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• Learning Distributionally Robust Models at Scale via Composite Optimization [45.47760229170775]
  We show how different variants of DRO are simply instances of a finite-sum composite optimization for which we provide scalable methods. We also provide empirical results that demonstrate the effectiveness of our proposed algorithm with respect to the prior art in order to learn robust models from very large datasets.
  arXiv  Detail & Related papers  (2022-03-17T20:47:42Z)
• Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models [40.08137765886609]
  We show that our model, called a graph structured surrogate model (GSSM), outperforms state-of-the-art methods in predicting environment dynamics. Our approach is able to obtain high returns, while allowing fast execution during deployment by avoiding test time policy gradient optimization.
  arXiv  Detail & Related papers  (2021-02-16T17:21:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.