txw1997

txw1997's Stars

• CyC2018/CS-Notes

  :books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

  175k5.3k56650.8k

• sty945/bank_interview

  :bank: 银行笔试面试经验分享及资料分享（help you pass the bank interview, and get a amazing bank offer!）

  5.1k3610627

• wvangansbeke/Unsupervised-Classification

  SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

  Language:Python1.4k54127267

• wyharveychen/CloserLookFewShot

  source code to ICLR'19, 'A Closer Look at Few-shot Classification'

  Language:Python1.1k1974269

• Duan-JM/awesome-papers-fewshot

  Collection for Few-shot Learning

  Language:TeX965398134

• facebookresearch/barlowtwins

  PyTorch implementation of Barlow Twins.

  Language:Python9582854128

• dev-sungman/Awesome-Self-Supervised-Papers

  Paper bank for Self-Supervised Learning

  57541459

• vikasverma1077/manifold_mixup

  Code for reproducing Manifold Mixup results (ICML 2019)

  Language:Python482111665

• timy90022/One-Shot-Object-Detection

  Implementation of One-Shot Object Detection with Co-Attention and Co-Excitation in Pytorch

  Language:Python423163183

• hytseng0509/CrossDomainFewShot

  Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)

  Language:Python32784262

• uvavision/Curriculum-Labeling

  [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning

  Language:Python1342521

• nupurkmr9/S2M2_fewshot

  Language:Python11253120

• Ze-Yang/Context-Transformer

  Context-Transformer: Tackling Object Confusion for Few-Shot Detection, AAAI 2020

  Language:Python104101613

• EdwinKim3069/XtarNet

  Language:Python101228

• mikucy/CS131

  My own solutions for CS131_fall1718 homework release

  Language:Jupyter Notebook813433

• shuozh/resLF

  Residual Networks for Light Field Image Super-Resolution

  Language:Python381106

• AntreasAntoniou/FewShotContinualLearning

  The original code for the paper "Benchmarks for Continual Few-Shot Learning".

  Language:Python34615

• facebookresearch/fewshotDatasetDesign

  The paper studies the problem of learning to recognize a new class of objects from a very small number of labeled images. This is called few-shot learning. Previous work in the literature focused on designing new algorithms that allow to learn to generalize to new unseen classes.In this work, we consider the impact of the dataset that we train on, and experiment with some dataset manipulations to see which trade-offs are important in the design of a dataset aimed at few-shot learning.

  Language:Jupyter Notebook25726

• tonysy/STANet-PyTorch

  A Dual Attention Network with Semantic Embedding for Few-shot Learning(AAAI 2019)

  1273