There are 2 repositories under cifar-100 topic.
Play deep learning with CIFAR datasets
Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"
Implementing Searching for MobileNetV3 paper using Pytorch
A PyTorch implementation for PyramidNets (Deep Pyramidal Residual Networks, https://arxiv.org/abs/1610.02915)
ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch
Torch implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search
Multi-task learning for image classification implemented in PyTorch.
Implementing Randomly Wired Neural Networks for Image Recognition, Using CIFAR-10 dataset, CIFAR-100 dataset
This is the PyTorch-0.4.0 implementation of few-shot learning on CIFAR-100 with graph neural networks (GNN)
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning
An implementation of MobileNetV3 with pyTorch
Collection of tensorflow notebooks tutorials for implementing some basic Deep Learning architectures.
Official PyTorch Implementation of Guarding Barlow Twins Against Overfitting with Mixed Samples
paddle cifar100 training
Selective Classification For Deep Neural Networks.
Convolutional Neural Networks using Tensorflow with Cifar-100 dataset
Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)
Residual Network Experiments with CIFAR Datasets.
A PyTorch framework for federated learning. This is a very basic framework.
VGG models from ILSVRC 2014
This repository provides experiment results for MobileNetV2 based on PyTorch.
We implement NNCLR and a novel clustering-based technique for contrastive learning that we call KMCLR. We show that applying a clustering technique to obtain prototype embeddings and using these prototypes to form positive pairs for contrastive loss can achieve performances on par with NNCLR on CIFAR-100 while storing 0.4% of the number of vectors.
Plug-and-play collaboration between specialized Tsetlin machines
Classification of CIFAR 100 Images - Transfer Learning - ResNet-50
It's a project to apply convolutional neural networks to the problem of image classification from the CIFAR 100 dataset.
Wide-Resnet-Ensemble implementation for CIFAR100 kaggle submission: >83.97% on kaggle private leaderboard
Vision transformer and CNN implementations for image classification using PyTorch.
Image Classification With Vision Transformer
Source code for "artificial intelligence introduction" course assignment.
ISEF 2023 (TEAM CANADA) PROJECT. Find the complete documentation and code in the README file linked here and below.