NeuralCollapseApplications/ImbalancedLearning
[NeurIPS 2022] The official code for our NeurIPS 2022 paper "Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?".
This project helps machine learning researchers and practitioners investigate the effectiveness of simplified classification methods for imbalanced datasets. It takes standard image datasets, particularly those with an uneven distribution of examples per category, and outputs trained models using different classification heads and loss functions. The models are evaluated on their ability to accurately classify examples from both common and rare categories.
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Use this if you are developing or evaluating deep learning models for classification tasks where certain categories have significantly fewer training examples than others.
Not ideal if you are looking for a general-purpose, out-of-the-box solution for balanced image classification or a tool for data preprocessing.
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Python
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Oct 12, 2022
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