Huan Wang (@MingSun-Tse)
github

MingSun-Tse

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  • Stars
    star
    1,359
  • Global Rank 22,829 (Top 0.8 %)
  • Followers 171
  • Following 48
  • Registered almost 9 years ago
  • Most used languages
    Python
         75.0 %
    TeX
         8.3 %
    Jupyter Notebook
         8.3 %
    Makefile     8.3 %
  • Location πŸ‡ΊπŸ‡Έ United States
  • Country Total Rank 6,787
  • Country Ranking
    Makefile     643
    TeX
         1,602
    Python
         2,919

Top repositories

1

Efficient-Deep-Learning

Collection of recent methods on (deep) neural network compression and acceleration.
925
star
2

Collaborative-Distillation

[CVPR'20] Collaborative Distillation for Ultra-Resolution Universal Style Transfer (PyTorch)
Python
180
star
3

Regularization-Pruning

[ICLR'21] Neural Pruning via Growing Regularization (PyTorch)
Python
73
star
4

ASSL

[NeurIPS'21 Spotlight] Aligned Structured Sparsity Learning for Efficient Image Super-Resolution (PyTorch)
Python
59
star
5

Awesome-Pruning-at-Initialization

[IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.
44
star
6

Smile-Pruning

A generic code base for neural network pruning, especially for pruning at initialization.
Python
30
star
7

Good-DA-in-KD

[NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective
Python
29
star
8

Why-the-State-of-Pruning-so-Confusing

[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning
29
star
9

smilelogging

Python logging package for easy reproducible experimenting in research
Python
25
star
10

TPP

[ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
Python
23
star
11

Awesome-Efficient-ViT

Recent Advances on Efficient Vision Transformers
22
star
12

SRP

[ICLR'22] PyTorch code for our paper "Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning"
Python
18
star
13

Caffe_IncReg

[IJCNN'19, IEEE JSTSP'19] Caffe code for our paper "Structured Pruning for Efficient ConvNets via Incremental Regularization"; [BMVC'18] "Structured Probabilistic Pruning for Convolutional Neural Network Acceleration"
Makefile
14
star
14

WritingTips

"Good scientific writing is not a matter of life and death; it is much more serious than that."
TeX
7
star
15

Efficient-NeRF

Python
7
star
16

UtilsHub

Jupyter Notebook
3
star
17

LowlevelVision

paper collection for low-level vision
3
star
18

AdversarialAttacks

2
star