This repository focuses on deep supervised (or semi-supervised) contrastive learning for limited datasets, such as few-shot learning scenarios. It utilizes labeled embedding data, which can be derived from pre-trained NLP, vision, or other algorithms capable of extracting embeddings. By applying deep feedforward networks (FFN), the approach refines new embeddings specifically tailored to a subset of classes and data. This technique enhances classification accuracy, even with a small number of data points per class.
roytalman/Deep_contrastive_embedding
Deep supervised conistrastive learning for small datasets (few shot learning). This repository takes labeled embedding data ,that could be extracted from pre-trained NLP, vision, or any other algorithm that extract embedding, and use deep FFN to learn new embedding that is fine-tuned for the current data. Th algorithm can improve classification per
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