[J. Imaging 2023] The official repository for paper CL3: Generalization of Contrastive Loss for Lifelong Learning J. Imaging 2023, 9(12), 259; https://doi.org/10.3390/jimaging9120259

A comprehensive ablation study conducted on the CIFAR-100 dataset. Three deep learning architectures: Convolutional Neural Networks (CNN), Gated Multilayer Perceptrons (gMLP), and Vision Transformers (ViT) are utilized. The project leverages PyTorch and PyTorch Lightning for model training and Optuna for hyperparameter tuning

A lightweight and extensible toolbox for image classification

Official PyTorch Implementation for the "Distilling Datasets Into Less Than One Image" paper.

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.

[AAAI 2023] Official PyTorch Code for "Curriculum Temperature for Knowledge Distillation"

Trained a multiclass classifer network using cifar100 dataset

Knowledge Distillation from VGG16 (teacher model) to MobileNet (student model)

classifying CIFAR-100 data set using MCSVM and Deep Conv Net

sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data

PyTorch implementation of 'ViT' (Dosovitskiy et al., 2020) and training it on CIFAR-10 and CIFAR-100

This project explores diverse approaches to image classification on the CIFAR-100 dataset. Starting from traditional CNNs combined with KNN classifiers, it progresses to ResNet50 with FCNN and culminates in the cutting-edge Vision Transformer (ViT) model.

IJCAI 2024, InfoMatch: Entropy neural estimation for semi-supervised image classification

Models with variable output classes designed for CIFAR-100

Feather is a module that enables effective sparsification of neural networks during training. This repository accompanies the paper "Feather: An Elegant Solution to Effective DNN Sparsification" (BMVC2023).

This repository includes official implementation and model weights of Data-Efficient Multi-Scale Fusion Vision Transformer.

Two case studies: effects of changing the learning rate on model perfomance for image classificaiton, and cardiac failure prediction using clinical data

Implementaiton of BSC-Densenet-121 in Pytorch from research paper "Adding Binary Search Connections to Improve DenseNet Performance".

Official PyTorch Code for "Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?" (https://arxiv.org/abs/2305.12954)

Kleines CNN zur Klassifizierung des CIFAR-10 Datensatzes.