Bi-Level Active Finetuning Framework for Sample Selection in Vision Tasks

Core Concepts

Proposing a Bi-Level Active Finetuning framework to balance diversity and uncertainty in sample selection.

Abstract

The article introduces the Bi-Level Active Finetuning Framework (BiLAF) to address challenges in active learning methods for sample selection. It focuses on balancing diversity and uncertainty by selecting central samples for diversity and boundary samples for uncertainty. The framework operates in two stages: Core Samples Selection and Boundary Samples Selection. The process starts with identifying pseudo-class centers, followed by denoising methods and iterative strategies for boundary sample selection without relying on ground-truth labels. Extensive experiments demonstrate the efficacy of the method, outperforming existing baselines significantly. The article also discusses related work, decision boundaries in neural networks, and the importance of boundary samples in classification tasks.

Stats

Our method achieves a remarkable improvement of nearly 3% on CIFAR100 and approximately 1% on ImageNet.
We set the core number K as 50(0.1%), 250(0.5%), 6405(0.5%) for CIFAR10, CIFAR100, and ImageNet separately.
In the boundary samples selection stage, we set nearest neighbors number k as 10, both removal ratio Prm and clustering fraction Pin as 10%, opponent penalty coefficient δ as 1.1.
For all three datasets, we resize images to 224 × 224 consistent with the pretraining for both data selection and supervised finetuning.

Quotes

"Our comprehensive experiments provide both qualitative and quantitative evidence of our method’s efficacy."
"Our approach is evaluated using three widely recognized datasets: CIFAR10, CIFAR100, and ImageNet-1k."
"Our objective is to choose the optimal sampling strategy to select the labeled set under the given budget to minimize the expectation error of the finetuned model."

Key Insights Distilled From

Boundary Matters

by Han Lu,Yiche... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10069.pdf

Deeper Inquiries

How can BiLAF be adapted to other domains beyond vision tasks

BiLAF's adaptability to domains beyond vision tasks lies in its fundamental framework of balancing diversity and uncertainty. This approach can be applied to various fields where data annotation is costly, such as natural language processing (NLP), healthcare, finance, and more. In NLP, for instance, BiLAF could aid in selecting the most informative text samples for model fine-tuning within a limited budget. By leveraging the concept of core sample selection for diversity and boundary sample selection for uncertainty, BiLAF can enhance active learning strategies in these domains.

What are potential drawbacks or limitations of focusing on boundary samples over diversity

Focusing solely on boundary samples over diversity may lead to certain drawbacks or limitations. One potential limitation is that by prioritizing boundary samples too heavily, there might be a risk of overlooking important information contained within diverse but non-boundary samples. Overemphasizing boundaries could result in models that are overly focused on specific decision regions at the expense of understanding the broader distribution of data points. Additionally, if not balanced properly with diverse samples, an excessive focus on boundaries may lead to overfitting or suboptimal generalization when faced with new data.

How might decision boundaries impact model generalizability across different datasets

Decision boundaries play a crucial role in determining how well a model generalizes across different datasets. The complexity and flexibility of decision boundaries impact a model's ability to capture intricate patterns present in diverse datasets accurately. Optimal decision boundaries contribute to improved generalizability by effectively separating classes while minimizing errors due to misclassification or noise. However, overly complex decision boundaries can lead to overfitting on training data and reduced performance on unseen test sets with different distributions.