Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL) - imbue

These findings highlight the importance of contrast between positive and negative examples when learning representations and help us gain a more fundamental understanding of how and why self-supervised learning works. The code used for this post can be found at https://github.com/untitled-ai/self_supervised. Machine learning is typically done in a supervised fashion: we use a dataset consisting of the inputs and “right answers” (outputs) to find the best function that maps from the input data onto the right answers. By contrast, in self-supervised 1 learning, no right answers are provided in the data set. Instead, we learn a function that maps the input data onto itself (ex: using the right half of an image to predict the left half of an image). This approach has proven successful in everything from language to images and audio. In fact, most recent language models, from word2vec to BERT and GPT-3, are examples of self-supervised approaches. More recently, this approach has had some in

Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL) - Imbue Article / Research Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL) 21 min read Last updated 16 Jun 2026 Josh Albrecht CTO, Co-founder Abe Fetterman Summary Why does self-supervised learning matter? State of the art in self-supervised learning Contrastive learning BYOL: self-supervised learning without contrastive learning? Not exactly. SimCLR MoCo BYOL Our surprising results Why did this happen? Performance for each variation Why batch normalizat

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