Play deep learning with CIFAR datasets
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Updated
Aug 27, 2020 - Python
Play deep learning with CIFAR datasets
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition (https://arxiv.org/pdf/2006.11538.pdf)
Wide Residual Networks (WideResNets) in PyTorch
Collection of Keras models used for classification
A PyTorch implementation for PyramidNets (Deep Pyramidal Residual Networks, https://arxiv.org/abs/1610.02915)
Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
Pytorch code for ICCV'23 paper. NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)
Various CNN models for CIFAR10 with Chainer
Wide Residual Networks in Keras
Source code of paper: (not available now)
Handwritten digit recognition with MNIST & Keras
Tensorflow - Very Deep Convolutional Neural Networks For Raw Waveforms - https://arxiv.org/pdf/1610.00087.pdf
Official PyTorch implementation of "Artist Style Transfer Via Quadratic Potential"
Meta-Zeta是一个基于强化学习的五子棋(Gobang)模型,主要用以了解AlphaGo Zero的运行原理的Demo,即神经网络是如何指导MCTS做出决策的,以及如何自我对弈学习。源码+教程
Python implementation of "Deep Residual Learning for Image Recognition" (http://arxiv.org/abs/1512.03385 - MSRA, winner team of the 2015 ILSVRC and COCO challenges).
Wide Residual Networks implemented in TensorLayer and TensorFlow.
Keras Implementation Residual Attention Network
Modified Residual U-Net (ResUnet) for Image Segmentation
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