MixMatch-pytorch and MixNMatch
About MixMatch-pytorch
YU1ut/MixMatch-pytorch
Code for "MixMatch - A Holistic Approach to Semi-Supervised Learning"
This project helps machine learning engineers and researchers classify images more accurately, especially when they have limited labeled data. By applying advanced semi-supervised learning techniques, it takes a small set of labeled images and a larger set of unlabeled images as input. It then outputs a trained image classification model with improved performance, particularly useful for tasks like object recognition.
About MixNMatch
WisconsinAIVision/MixNMatch
Pytorch implementation of MixNMatch
This project helps graphic designers, advertisers, or creative professionals generate new, realistic images by combining distinct elements from various source images. You can input separate images for an object's pose, background, shape, and color to create a unique composite image. This is ideal for quickly iterating on visual concepts or creating diverse content without needing to manually edit each element.
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