cifar10
There are 812 repositories under cifar10 topic.
haitongli/knowledge-distillation-pytorch
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
bearpaw/pytorch-classification
Classification with PyTorch.
hysts/pytorch_image_classification
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet
yoshitomo-matsubara/torchdistill
A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆25 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.
Hyperparticle/one-pixel-attack-keras
Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet
tysam-code/hlb-CIFAR10
Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)
BIGBALLON/cifar-10-cnn
Play deep learning with CIFAR datasets
huyvnphan/PyTorch_CIFAR10
Pretrained TorchVision models on CIFAR10 dataset (with weights)
alvinwan/neural-backed-decision-trees
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
leftthomas/SimCLR
A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"
VITA-Group/AutoGAN
[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang
joaopauloschuler/neural-api
CAI NEURAL API - Pascal based deep learning neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA.
junyuseu/pytorch-cifar-models
3.41% and 17.11% error on CIFAR-10 and CIFAR-100
naszilla/naszilla
Naszilla is a Python library for neural architecture search (NAS)
kiryor/nnPUlearning
Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10
chenyaofo/pytorch-cifar-models
Pretrained models on CIFAR10/100 in PyTorch
leaderj1001/BottleneckTransformers
Bottleneck Transformers for Visual Recognition
hiwonjoon/tf-vqvae
Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
stanford-futuredata/dawn-bench-entries
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
mafda/generative_adversarial_networks_101
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
tugstugi/pytorch-speech-commands
Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge
lonePatient/lookahead_pytorch
pytorch implement of Lookahead Optimizer
Kedreamix/Pytorch-Image-Classification
用于pytorch的图像分类，包含多种模型方法，比如AlexNet，VGG，GoogleNet，ResNet，DenseNet等等，包含可完整运行的代码。除此之外，也有colab的在线运行代码，可以直接在colab在线运行查看结果。也可以迁移到自己的数据集进行迁移学习。
ethanhe42/resnet-cifar10-caffe
ResNet-20/32/44/56/110 on CIFAR-10 with Caffe
tyui592/Pruning_filters_for_efficient_convnets
PyTorch implementation of "Pruning Filters For Efficient ConvNets"
mitmul/chainer-cifar10
Various CNN models for CIFAR10 with Chainer
zeiss-microscopy/BSConv
Reference implementation for Blueprint Separable Convolutions (CVPR 2020)
9310gaurav/virtual-adversarial-training
Pytorch implementation of Virtual Adversarial Training
junyuseu/ResNet-on-Cifar10
Reimplementation ResNet on cifar10 with caffe
xilaili/AOGNet
Code for CVPR 2019 paper: " Learning Deep Compositional Grammatical Architectures for Visual Recognition"
IShengFang/SpectralNormalizationKeras
Spectral Normalization for Keras Dense and Convolution Layers
aa-samad/conv_snn
Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" paper
ypwhs/deeplearning-models
各种深度学习结构、模型和技巧的集合
chengtan9907/Co-learning
The official implementation of the ACM MM'2021 paper Co-learning: Learning from noisy labels with self-supervision.
KentoNishi/Augmentation-for-LNL
[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".
microsoft/petridishnn
Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search