nayeemrizve/invariance-equivariance
"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning" by Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah (CVPR 2021)
This project helps machine learning researchers improve how their image classification models learn from very limited data. By combining different learning techniques, it takes a small set of labeled images as input and produces a more accurate model capable of recognizing new categories with only a few examples. This is useful for researchers and practitioners in computer vision and AI development working on specialized image recognition tasks where large datasets are hard to acquire.
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Use this if you are a machine learning researcher developing image classification systems and need to train models effectively when you have very few labeled images available for new categories.
Not ideal if you are looking for an out-of-the-box solution for general image classification with abundant data, or if you are not working in the field of few-shot learning research.
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Language
Python
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MIT
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Last pushed
Mar 14, 2023
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