Popular GitHub Repositories for Cifar 10

Using Capsule Networks in GANS to generate very realistic fake images that could perhaps be used for deepfakes

all methods of pytorch quantization based on resnet50

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

A numpy based CNN implementation for classifying images

Federated Learning: Parameter Server doing aggregation of updates to a model coming from clients participating in a Federated Learning setup. See also the Android application companion at https://github.com/mccorby/PhotoLabeller

Implementation of "Cost-Effective Active Learning for Deep Image Classification" paper

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 of Cifar-10 dataset using a convolutional neural network. The hyper parameters to construct CNN architecture are optimized using genetic algorithm.

A performance benchmark of recent image classification models in deep learning

Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)

A PyTorch implementation of "Adapting Neural Architectures Between Domains" in NeurIPS 2020.

2021 Spring (Deep Learning) 深度学习课程作业及资料

A PyTorch framework for federated learning. This is a very basic framework.

Model training examples for NeoPulse AI Studio AMI

Different template codes for Deep Learning with PyTorch.

This repository is an implementation of https://link.springer.com/chapter/10.1007/978-3-030-72699-7_35 article. it uses evolutionary strategy (NSGA-II algorithm specificially) to configure image filters parameters in order to attack adversarially to a neural network.

In this we fine-tune MLP with LoRA and DoRA on CIFAR-10 Dataset

Implementing FedAvg (Federated Average) based on CIFAR-10 dataset 基于CIFAR-10数据集实现FedAvg

Plug-and-play collaboration between specialized Tsetlin machines

This is the repository of my project reviewing the findings of the paper "The Dimpled Manifold Model of Adversarial Examples in Machine Learning" by Shamir et.al.