Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm

• URL: http://arxiv.org/abs/2303.14382v1
• Date: Sat, 25 Mar 2023 07:17:03 GMT
• Title: Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
• Authors: Yichen Xie, Han Lu, Junchi Yan, Xiaokang Yang, Masayoshi Tomizuka, Wei Zhan
• Abstract summary: This paper focuses on the selection of samples for annotation in the pretraining-finetuning paradigm. We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool. Extensive experiments show the leading performance and high efficiency of ActiveFT superior to baselines on both image classification and semantic segmentation.
• Score: 132.9949120482274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a popular paradigm in multiple computer vision tasks. Previous research has covered both the unsupervised pretraining and supervised finetuning in this paradigm, while little attention is paid to exploiting the annotation budget for finetuning. To fill in this gap, we formally define this new active finetuning task focusing on the selection of samples for annotation in the pretraining-finetuning paradigm. We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space. We prove that the Earth Mover's distance between the distributions of the selected subset and the entire data pool is also reduced in this process. Extensive experiments show the leading performance and high efficiency of ActiveFT superior to baselines on both image classification and semantic segmentation. Our code is released at https://github.com/yichen928/ActiveFT.

Related papers

• Scalable Fine-tuning from Multiple Data Sources:A First-Order Approximation Approach [17.79010397902909]
  We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from $n$ auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-thought fine-tuning. This paper introduces a new algorithm to estimate model fine-tuning performances without repeated training.
  arXiv  Detail & Related papers  (2024-09-28T21:26:50Z)
• Training-Free Unsupervised Prompt for Vision-Language Models [27.13778811871694]
  We propose Training-Free Unsupervised Prompts (TFUP) to preserve inherent representation capabilities and enhance them with a residual connection to similarity-based prediction probabilities. TFUP achieves surprising performance, even surpassing the training-base method on multiple classification datasets. Our TFUP-T achieves new state-of-the-art classification performance compared to unsupervised and few-shot adaptation approaches on multiple benchmarks.
  arXiv  Detail & Related papers  (2024-04-25T05:07:50Z)
• Boundary Matters: A Bi-Level Active Finetuning Framework [100.45000039215495]
  The concept of active finetuning has emerged, aiming to select the most appropriate samples for model finetuning within a limited budget. Traditional active learning methods often struggle in this setting due to their inherent bias in batch selection. We propose a Bi-Level Active Finetuning framework to select the samples for annotation in one shot, which includes two stages: core sample selection for diversity, and boundary sample selection for uncertainty.
  arXiv  Detail & Related papers  (2024-03-15T07:19:15Z)
• VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness [56.87603097348203]
  VeCAF uses labels and natural language annotations to perform parametric data selection for PVM finetuning. VeCAF incorporates the finetuning objective to select significant data points that effectively guide the PVM towards faster convergence. On ImageNet, VeCAF uses up to 3.3x less training batches to reach the target performance compared to full finetuning.
  arXiv  Detail & Related papers  (2024-01-15T17:28:37Z)
• ActiveDC: Distribution Calibration for Active Finetuning [36.64444238742072]
  We propose a new method called ActiveDC for the active finetuning tasks. We calibrate the distribution of the selected samples by exploiting implicit category information in the unlabeled pool. The results indicate that ActiveDC consistently outperforms the baseline performance in all image classification tasks.
  arXiv  Detail & Related papers  (2023-11-13T14:35:18Z)
• Approximated Prompt Tuning for Vision-Language Pre-trained Models [54.326232586461614]
  In vision-language pre-trained models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks. We propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning.
  arXiv  Detail & Related papers  (2023-06-27T05:43:47Z)
• Prompt Tuning for Parameter-efficient Medical Image Segmentation [79.09285179181225]
  We propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes. We demonstrate that the resulting neural network model is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models.
  arXiv  Detail & Related papers  (2022-11-16T21:55:05Z)
• Beyond Transfer Learning: Co-finetuning for Action Localisation [64.07196901012153]
  We propose co-finetuning -- simultaneously training a single model on multiple upstream'' and downstream'' tasks. We demonstrate that co-finetuning outperforms traditional transfer learning when using the same total amount of data. We also show how we can easily extend our approach to multiple upstream'' datasets to further improve performance.
  arXiv  Detail & Related papers  (2022-07-08T10:25:47Z)

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.