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PyTorch implementation of PatchAutoAugment
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
PyTorch implementation of deep reinforcement learning algorithms
A3C LSTM Atari with Pytorch plus A3G design
Code to support training, evaluating and interacting neural network dialog models, and training them with reinforcement learning. Code to deploy a web server which hosts the models live online is a…
Code for CoRL 2019 paper: HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
Deep Q-Learning Network in pytorch (not actively maintained)
Code implementing the experiments described in the paper "On The Power of Curriculum Learning in Training Deep Networks" by Hacohen & Weinshall (ICML 2019)
Algorithms for curriculum learning. The code of the "Mastering Rate based Curriculum Learning" paper.
3D ResNets for Action Recognition (CVPR 2018)
Look-into-Object: Self-supervised Structure Modeling for Object Recognition (CVPR 2020)
This is a pytorch re-implementation of Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition
A PyTorch implementation of WS-DAN (Weakly Supervised Data Augmentation Network) for FGVC (Fine-Grained Visual Classification)
A method for Fine-Grained image classification
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
A PyTorch implementation for PyramidNets (Deep Pyramidal Residual Networks, https://arxiv.org/abs/1610.02915)
PyTorch implementation of shake-shake regularization
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
An elegant PyTorch deep reinforcement learning library.
model learning and test for tiny-imageNet
🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017
A framework for data augmentation for 2D and 3D image classification and segmentation
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
A treasure chest for visual classification and recognition powered by PaddlePaddle