A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
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Updated
Mar 25, 2023 - Python
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
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.
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet
Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)
Play deep learning with CIFAR datasets
Pretrained TorchVision models on CIFAR10 dataset (with weights)
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"
[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang
3.41% and 17.11% error on CIFAR-10 and CIFAR-100
Naszilla is a Python library for neural architecture search (NAS)
Pretrained models on CIFAR10/100 in PyTorch
Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10
Bottleneck Transformers for Visual Recognition
Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge
Various CNN models for CIFAR10 with Chainer
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