62 Open source projects that are alternatives of or similar to Pytorch_shake_shake

Bottleneck Transformers for Visual Recognition

Implementation of Conv-based and Vit-based networks designed for CIFAR.

A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.

Implement popular models by different DL framework. Such as tensorflow and caffe

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

Play deep learning with CIFAR datasets

Generative Adversarial Networks in TensorFlow 2.0

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

Federated Learning mini-framework with Keras

Replace FC2, LeNet-5, VGG, Resnet, Densenet's full-connected layers with MPO

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

Image Classification on CIFAR-10 Dataset using Multi Layer Perceptrons in Python from Scratch.

TensorBasicModel

PyTorch implementation of Adversarially Learned Inference (BiGAN).

A implementation of DCGAN (Deep Convolutional Generative Adversarial Networks) for CIFAR10 image

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

A Gluon implement of Residual Attention Network. Best acc on cifar10-97.78%.

Cifar-10 CNN implementation using TensorFlow library with 20% error.

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

My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition

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

The implementation of Relativistic average GAN with Keras

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

practice on CIFAR10 with Keras

Various implementations and projects on CNN, RNN, LSTM, GAN, etc

Speech commands recognition with PyTorch

Theano implementation of XNOR-Net

Unofficial pytorch implementation of ReZero in ResNet

Label Embedding Network

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

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

PC-DARTS (PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search, published in ICLR 2020) implemented in Tensorflow 2.0+. This is an unofficial implementation.

Classification with PyTorch.

A numpy based CNN implementation for classifying images

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

anomaly detection by one-class SVM

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

Keras implementation of temporal ensembling(semi-supervised learning)

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

PyTorch implementation of shake-drop regularization

Pytorch implementation of Virtual Adversarial Training

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

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.

Pretrained TorchVision models on CIFAR10 dataset (with weights)

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

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

Predict CIFAR-10 labels with 88% accuracy using keras.

PyTorch-based modular, configuration-driven framework for knowledge distillation. 🏆18 methods including SOTA are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy.

Reference implementation for Blueprint Separable Convolutions (CVPR 2020)

pytorch implement of Lookahead Optimizer

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage…

Various CNN models for CIFAR10 with Chainer

tensorflow implementation of Exploring Randomly Wired Neural Networks for Image Recognition

Train DenseNet on Cifar-10 based on Keras

Spectral Normalization for Keras Dense and Convolution Layers

CAI NEURAL API - Pascal based neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA.

[ICLR'19] Complement Objective Training

Tensorflow ResNet implementation on cifar10

ResNet for Cifar10