Shypd.aiThe largest open index of AI toolsimbalanced-semi-self is an Open Source & Models tool that provides implementation code for improving class-imbalanced learning. It leverages semi-supervision with unlabeled data and self-supervision techniques to address long-tailed learning challenges.
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imbalanced-semi-self is an open-source GitHub repository offering implementation code for the paper "Rethinking the Value of Labels for Improving Class-Imbalanced Learning" presented at NeurIPS 2020. This tool focuses on enhancing performance on imbalanced (long-tailed) datasets by utilizing both semi-supervised learning (with unlabeled data) and self-supervised pre-training. It demonstrates how these techniques can improve class separation and mitigate tail class leakage, even with varying imbalanceness in labeled and unlabeled data. The repository includes code for training models with extra unlabeled data, self-supervised pre-training using Rotation prediction or MoCo, and network training with SSP models, supporting datasets like CIFAR, SVHN, ImageNet-LT, and iNaturalist 2018. It provides detailed instructions for installation, data preparation, pseudo-label generation, and testing pre-trained checkpoints.
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