psellcam/LaplaceNet
A PyTorch Implementation of LaplaceNet:A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification
This project offers a method for deep semi-supervised image classification. It helps machine learning practitioners classify large image datasets efficiently, even when only a small portion of the images are labeled. You provide your image data (like CIFAR-10/100 or Mini-ImageNet) with some labels, and it outputs a trained classification model capable of categorizing the unlabeled images.
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Use this if you need to classify large image datasets and have a limited budget for manual data labeling, aiming to achieve high accuracy with fewer labeled examples.
Not ideal if your dataset is not image-based, or if you prefer a fully supervised learning approach with a completely labeled dataset.
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Feb 08, 2022
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