Augmentation Policy Generation for Image Classification Using Large Language Models

• URL: http://arxiv.org/abs/2410.13453v1
• Date: Thu, 17 Oct 2024 11:26:10 GMT
• Title: Augmentation Policy Generation for Image Classification Using Large Language Models
• Authors: Ant Duru, Alptekin Temizel,
• Abstract summary: We propose a strategy that uses large language models to automatically generate efficient augmentation policies. The proposed method was evaluated on medical imaging datasets, showing a clear improvement over state-of-the-art methods.
• Score: 3.038642416291856
• License:
• Abstract: Automated data augmentation methods have significantly improved the performance and generalization capability of deep learning models in image classification. Yet, most state-of-the-art methods are optimized on common benchmark datasets, limiting their applicability to more diverse or domain-specific data, such as medical datasets. In this paper, we propose a strategy that uses large language models to automatically generate efficient augmentation policies, customized to fit the specific characteristics of any dataset and model architecture. The proposed method iteratively interacts with an LLM to obtain and refine the augmentation policies on model performance feedback, creating a dataset-agnostic data augmentation pipeline. The proposed method was evaluated on medical imaging datasets, showing a clear improvement over state-of-the-art methods. The proposed approach offers an adaptive and scalable solution. Although it increases computational cost, it significantly boosts model robustness, automates the process, and minimizes the need for human involvement during model development.

Related papers

• Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations [1.5723316845301678]
  This report introduces a novel methodology for training with augmentations to enhance model robustness and performance in such conditions. We present a comprehensive framework that includes identifying weak spots in Machine Learning models, selecting suitable augmentations, and devising effective training strategies. Experimental results demonstrate improvements in model performance, as measured by commonly used metrics such as mean Average Precision (mAP) and mean Intersection over Union (mIoU) on open-source object detection and semantic segmentation models and datasets.
  arXiv  Detail & Related papers  (2024-08-30T14:15:48Z)
• 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)
• Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions [6.2719115566879236]
  Diffusion Models (DMs) have emerged as a powerful tool for image data augmentation. DMs generate realistic and diverse images by learning the underlying data distribution. Current challenges and future research directions in the field are discussed.
  arXiv  Detail & Related papers  (2024-07-04T18:06:48Z)
• A Comprehensive Survey on Data Augmentation [55.355273602421384]
  Data augmentation is a technique that generates high-quality artificial data by manipulating existing data samples. Existing literature surveys only focus on a certain type of specific modality data. We propose a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities.
  arXiv  Detail & Related papers  (2024-05-15T11:58:08Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Phased Data Augmentation for Training a Likelihood-Based Generative Model with Limited Data [0.0]
  Generative models excel in creating realistic images, yet their dependency on extensive datasets for training presents significant challenges. Current data-efficient methods largely focus on GAN architectures, leaving a gap in training other types of generative models. "phased data augmentation" is a novel technique that addresses this gap by optimizing training in limited data scenarios without altering the inherent data distribution.
  arXiv  Detail & Related papers  (2023-05-22T03:38:59Z)
• A Generic Approach for Enhancing GANs by Regularized Latent Optimization [79.00740660219256]
  We introduce a generic framework called em generative-model inference that is capable of enhancing pre-trained GANs effectively and seamlessly. Our basic idea is to efficiently infer the optimal latent distribution for the given requirements using Wasserstein gradient flow techniques.
  arXiv  Detail & Related papers  (2021-12-07T05:22:50Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)
• Cross-Modal Generative Augmentation for Visual Question Answering [34.9601948665926]
  This paper introduces a generative model for data augmentation by leveraging the correlations among multiple modalities. The proposed model is able to quantify the confidence of augmented data by its generative probability, and can be jointly updated with a downstream pipeline.
  arXiv  Detail & Related papers  (2021-05-11T04:51:26Z)
• Model-Augmented Actor-Critic: Backpropagating through Paths [81.86992776864729]
  Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator. We show how to make more effective use of the model by exploiting its differentiability.
  arXiv  Detail & Related papers  (2020-05-16T19:18:10Z)

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.