QJH's repositories
deeplearning-models
A collection of various deep learning architectures, models, and tips
dont-stop-pretraining
Code associated with the Don't Stop Pretraining ACL 2020 paper
ape210k
This is the repository of the Ape210K dataset and baseline models.
AutoDL-Projects
Automated deep learning algorithms implemented in PyTorch or Tensorflow.
CORnet
CORnet: Modeling the Neural Mechanisms of Core Object Recognition
dynamic-coattention-network-plus
Dynamic Coattention Network Plus (DCN+) TensorFlow implementation. Question answering using Deep NLP.
ganspace
Discovering Interpretable GAN Controls
image-super-resolution
Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
InterpretEval
Interpretable Evaluation for (Almost) All NLP Tasks
iSeeBetter
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
Meta-SR-Pytorch
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution (CVPR2019)
neural-chat
An AI chatbot using seq2seq
neural_chat
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 available at: https://github.com/asmadotgh/neural_chat_web
predictive-filter-flow
Predictive Filter Flow for fully/self-supervised learning on various vision tasks
PSPL
The implementation of ICASSP 2020 paper "Pixel-level self-paced learning for super-resolution"
ReID_tutorial_slides
《深度学习与行人重识别》课程课件
SelectiveMasking
Source code for "Train No Evil: Selective Masking for Task-Guided Pre-Training"
tag-based-multi-span-extraction
The official implementation of EMNLP 2020, "A Simple and Effective Model for Answering Multi-span Questions".
tranX
A general-purpose neural semantic parser for mapping natural language queries into machine executable code