This implementation is based on PyTorch >= 1.0.0. Small dataset (including Cora, Citeseer, and Pubmed) are located in the data folder.
Please use the jupyter notebook files localed in the ./jupyter_notebooks. Please copy the file you want to run to the root folder, i.e., ./DGCN/CODE_WANT_TO_RUN.ipynb, then directly run it using jupyter notebook.
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Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC
Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create
SegSwap Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery" [PDF] [Project page] If our project
visemenet-inference Inference Demo of "VisemeNet-tensorflow" VisemeNet is an audio-driven animator centric speech animation driving a JALI or standard
Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. A
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