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Table of Contents
1. Introduction
2. Model
3. Method
3.1. Overview
3.2. Loss Prediction Module
3.3. Learning Loss
4. Contributions
5. Related Research
Summary
The document discusses the problem of learning loss for active learning in deep neural networks. It introduces a novel active learning method with a loss prediction module that is task-agnostic and efficient for deep networks. The method involves selecting data points with high losses to improve model performance. The loss prediction module is designed to imitate the loss defined in the target model, and the learning process involves jointly training the target model and the loss prediction module. The document presents detailed insights into the proposed method, its architecture, and the process of learning the loss prediction module. Experimental results demonstrate the effectiveness of the method in tasks such as image classification, human pose estimation, and object detection.