Boundary Matters: A Bi-Level Active Finetuning Framework
The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields, yet it faces the significant challenge of high sample annotation costs. To mitigate this, 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. Furthermore, the recent active finetuning approach has primarily concentrated on aligning the distribution of selected subsets with the overall data pool, focusing solely on diversity. In this paper, 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. The process begins with the identification of pseudo-class centers, followed by an innovative denoising method and an iterative strategy for boundary sample selection in the high-dimensional feature space, all without relying on ground-truth labels. Our comprehensive experiments provide both qualitative and quantitative evidence of our method's efficacy, outperforming all the existing baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningDenoisingDiversitySimilar Papers 제목 키워드 기반
Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations
We present an evaluation framework for interactive dialogue assessment in the context of English as a Second Language (ESL) speakers. Our framework collects dialogue-level interactivity labels (e.g., topic management; 4 …
ManagementInteractive Boundary Prediction for Object Selection
Interactive image segmentation is critical for many image editing tasks. While recent advanced methods on interactive segmentation focus on the region-based paradigm, more traditional boundary-based methods such as Intel…
DecoderImage SegmentationInteractive SegmentationObject+3When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
Contrastive learning (CL) can learn generalizable feature representations and achieve the state-of-the-art performance of downstream tasks by finetuning a linear classifier on top of it. However, as adversarial robustnes…
Adversarial RobustnessContrastive Learningimage-classificationImage Classification+1FACT: A Simple and Efficient Framework for Active Finetuning
The main goal of active finetuning is to improve a pretrained model's performance on a specific task or domain by finetuning it with carefully selected informative or challenging data. Previous research has predominantly…
Image ClassificationActive LearningActive Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
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…
Diversityimage-classificationImage ClassificationSemantic Segmentation