ilia10000/LO-Shot
Papers and code related to 'Less Than One'-Shot (LO-Shot) Learning
This collection of research and code explores "less than one-shot" (LO-Shot) learning, a method where a classification model can recognize more categories than the number of examples it was shown. It takes in a small number of data examples and outputs models capable of classifying many more categories. This is designed for machine learning researchers and practitioners who need to train accurate models with extremely limited training data.
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Use this if you need to build machine learning models that can distinguish between many different classes, even when you have very few training examples for each class.
Not ideal if you already have large, well-labeled datasets or if your primary focus is on standard deep learning architectures with abundant data.
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Jul 21, 2024
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