Critic Loss for Image Classification

• URL: http://arxiv.org/abs/2409.15565v1
• Date: Mon, 23 Sep 2024 21:41:33 GMT
• Title: Critic Loss for Image Classification
• Authors: Brendan Hogan Rappazzo, Aaron Ferber, Carla Gomes,
• Abstract summary: CrtCl formulates image classification training in a generator-critic framework. CrtCl represents a learned loss method for accuracy, alleviating the negative side effects of using cross-entropy loss. We study the effectiveness of CrtCl in low-labeled data regimes, and in the context of active learning.
• Score: 0.6437284704257459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern neural network classifiers achieve remarkable performance across a variety of tasks; however, they frequently exhibit overconfidence in their predictions due to the cross-entropy loss. Inspired by this problem, we propose the \textbf{Cr}i\textbf{t}ic Loss for Image \textbf{Cl}assification (CrtCl, pronounced Critical). CrtCl formulates image classification training in a generator-critic framework, with a base classifier acting as a generator, and a correctness critic imposing a loss on the classifier. The base classifier, acting as the generator, given images, generates the probability distribution over classes and intermediate embeddings. The critic model, given the image, intermediate embeddings, and output predictions of the base model, predicts the probability that the base model has produced the correct classification, which then can be back propagated as a self supervision signal. Notably, the critic does not use the label as input, meaning that the critic can train the base model on both labeled and unlabeled data in semi-supervised learning settings. CrtCl represents a learned loss method for accuracy, alleviating the negative side effects of using cross-entropy loss. Additionally, CrtCl provides a powerful way to select data to be labeled in an active learning setting, by estimating the classification ability of the base model on unlabeled data. We study the effectiveness of CrtCl in low-labeled data regimes, and in the context of active learning. In classification, we find that CrtCl, compared to recent baselines, increases classifier generalization and calibration with various amounts of labeled data. In active learning, we show our method outperforms baselines in accuracy and calibration. We observe consistent results across three image classification datasets.

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