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@COMP6248-Reproducability-Challenge

COMP6248-Reproducability-Challenge

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  1. selfsupervised-denoising selfsupervised-denoising Public

    High-Quality Self-Supervised Deep Image Denoising - Unofficial PyTorch implementation of the NeurIPS 2019 paper

    Python 57 8

  2. music-transformer-comp6248 music-transformer-comp6248 Public

    Re-implementation of music transformer paper published for ICLR 2019.

    Jupyter Notebook 49 9

  3. HOW-MUCH-POSITION-INFORMATION-DO-CONVOLUTIONAL-NEURAL-NETWORKS-ENCODE- HOW-MUCH-POSITION-INFORMATION-DO-CONVOLUTIONAL-NEURAL-NETWORKS-ENCODE- Public

    Reproduce the paper named 'HOW MUCH POSITION INFORMATION DO CONVOLUTIONAL NEURAL NETWORKS ENCODE?', which published as a conference paper at ICLR 2020.

    Jupyter Notebook 23 5

  4. Reproduction-of-Probabilistic-binary-neural-networks Reproduction-of-Probabilistic-binary-neural-networks Public

    Reproduction of Probabilistic binary neural networks

    Jupyter Notebook 10 3

  5. Self-supervised-deep-learning-on-point-clouds-by-reconstructing-space Self-supervised-deep-learning-on-point-clouds-by-reconstructing-space Public

    Repository containing the code used in the reproducibility challenge coursework of the COMP6248 module at the University of Southampton

    Jupyter Notebook 7

  6. SVAE SVAE Public

    Spatial Variational Autoencoder

    Jupyter Notebook 6

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