WHQ1111's starred repositories
generalize-unseen-domains-PyTorch
PyTorch Implementation: Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018. Origin Tensorflow Implementation: https://github.com/ricvolpi/generalize-unseen-domains
MLAPP_CN_CODE
《Machine Learning: A Probabilistic Perspective》（Kevin P. Murphy）中文翻译和书中算法的Python实现。
Awesome-Few-Shot-Image-Generation
A curated list of papers, code and resources pertaining to few-shot image generation.
Deep-Learning-Models
Deep Learning Models
CrossDomainFewShot
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)
MetaLearning4NLP-Papers
A list of recent papers about Meta / few-shot learning methods applied in NLP areas.
FewShotContinualLearningDataProvider
The original code for the data providers and the datasets of the paper "Defining Benchmarks for Continual Few-Shot Learning".
aaai-template
latex template for various conferences, as well as wise-man's overleaf (overleaf is terrible!)
Dynamic-Few-Shot-Visual-Learning-without-Forgetting
pytorch simple implement for "Dynamic Few-Shot Visual Learning without Forgetting" in Jupyter Version
tps_stn_pytorch
PyTorch implementation of Spatial Transformer Network (STN) with Thin Plate Spline (TPS)
Pytorch-STN
Spatial Transformer Networks in Pytorch.
dlsys-solution
My solutions to the assignments of dlsys course (CSE599G1: Deep Learning System Spring 2017)
MetaOptNet
Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
Meta-Learning-via-Feature-Label-Memory-Network
This is a code for URP research project I have been doing for 6 months (Jan - July 2017). The project title was "Developing new mechanisms to Neural Turing Machines (NTMs) for one-shot learning".
Gumbel_Softmax_VAE
PyTorch implementation of a Variational Autoencoder with Gumbel-Softmax Distribution
BUAAthesis
北航毕设论文LaTeX模板
gumbel-softmax
categorical variational autoencoder using the Gumbel-Softmax estimator
NLPdatalist
a collection of various NLP datasets with references