| aaron-xichen/pytorch-playground |
2,366 |
|
0 |
0 |
over 3 years ago |
0 |
|
9 |
mit |
Python |
| Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet) |
| taki0112/Densenet-Tensorflow |
471 |
|
0 |
0 |
about 7 years ago |
0 |
|
15 |
mit |
Python |
| Simple Tensorflow implementation of Densenet using Cifar10, MNIST |
| yaodongyu/TRADES |
459 |
|
0 |
0 |
about 3 years ago |
0 |
|
4 |
mit |
Python |
| TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization) |
| wy1iu/LargeMargin_Softmax_Loss |
297 |
|
0 |
0 |
over 7 years ago |
0 |
|
10 |
other |
C++ |
| Implementation for <Large-Margin Softmax Loss for Convolutional Neural Networks> in ICML'16. |
| kiryor/nnPUlearning |
253 |
|
0 |
0 |
over 3 years ago |
0 |
|
2 |
other |
Python |
| Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10 |
| tensorlayer/awesome-tensorlayer |
212 |
|
0 |
0 |
about 6 years ago |
0 |
|
1 |
cc0-1.0 |
|
| A curated list of dedicated resources and applications |
| hiwonjoon/tf-vqvae |
205 |
|
0 |
0 |
about 8 years ago |
0 |
|
1 |
|
Jupyter Notebook |
| Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE). |
| mafda/generative_adversarial_networks_101 |
190 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets. |
| eladhoffer/TripletNet |
174 |
|
0 |
0 |
almost 9 years ago |
0 |
|
4 |
mit |
Lua |
| Deep metric learning using Triplet network |
| narumiruna/efficientnet-pytorch |
108 |
|
0 |
0 |
about 2 years ago |
0 |
|
4 |
mit |
Python |
| A PyTorch implementation of "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks". |
