cifar10

Classification with PyTorch.

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

Play deep learning with CIFAR datasets

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang

A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"

3.41% and 17.11% error on CIFAR-10 and CIFAR-100

Pretrained TorchVision models on CIFAR10 dataset (with weights)

DAWNBench: An End-to-End Deep Learning Benchmark and Competition

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

Naszilla is a Python library for neural architecture search (NAS)

Bottleneck Transformers for Visual Recognition

ResNet-20/32/44/56/110 on CIFAR-10 with Caffe

pytorch implement of Lookahead Optimizer

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge

Code for CVPR 2019 paper: " Learning Deep Compositional Grammatical Architectures for Visual Recognition"

Various CNN models for CIFAR10 with Chainer

Reimplementation ResNet on cifar10 with caffe

Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search

Pytorch implementation of Virtual Adversarial Training

Spectral Normalization for Keras Dense and Convolution Layers

A PyTorch implementation of shake-shake

各种深度学习结构、模型和技巧的集合

Reference implementation for Blueprint Separable Convolutions (CVPR 2020)