controlgay

controlgay's Stars

• microsoft/robustlearn

  Robust machine learning for responsible AI

  Language:Python45957

• lijin118/CGDM

  Codes for "Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation" in CVPR 2021

  Language:Python6614

• facebookresearch/DomainBed

  DomainBed is a suite to test domain generalization algorithms

  Language:Python1.4k298

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.5k3.8k

• Ritam-Guha/Feature-Selection

  It contains some of the novel feature selection algorithms I've developed

  Language:MATLAB136

• qinenergy/syn2real

  Code and data for our paper on IEEE-TIE: Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis

  Language:Jupyter Notebook3810

• google-research/google-research

  Google Research

  Language:Jupyter Notebook34.4k7.9k

• liguge/DCPN

  Self-supervised diagnosis of rotating machinery faults using a deep convolutional probability network

  2

• liguge/AAU_Net_Improved

  The improved code of AAU-Net

  2

• Luo-ziming/Semi-Supervised-Learning

  通过差分矩阵获得到三个特征约简子集，作为Tri-Training的输入，与单个分类器相比能够提升分类准确率。

  Language:Python2

• oikosohn/compound-loss-pytorch

  Compound loss for PyTorch

  Language:Python459

• JohnMasoner/unified-focal-loss-pytorch

  The unofficial implementation for "Unified Focal Loss: Generalising Dice and Cross Entropy-based Losses to Handle Class Imbalanced Medical Image Segmentation"

  Language:Python7

• lab206/AdaptiveClick

  [TNNLS 2024] AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation

  Language:Python25

• ywatanabe1989/custom_losses_pytorch

  Custom loss functions to use in (mainly) PyTorch.

  Language:Python373

• aws-samples/amazon-sagemaker-custom-loss-function

  This code shows how to train a model in Amazon SageMaker using a custom loss function for a binary classification problem in which the costs of different kinds of misclassification are very different.

  Language:Jupyter Notebook133

• jhwjhw0123/Imbalance-XGBoost

  XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions

  Language:Python30869

• shruti-jadon/Semantic-Segmentation-Loss-Functions

  This Repository is implementation of majority of Semantic Segmentation Loss Functions

  Language:Python55784

• eeyhsong/EEG-Conformer

  EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.

  Language:Python45762

• eeyhsong/EEG-Transformer

  i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.

  Language:Python26929

• AntixK/PyTorch-VAE

  A Collection of Variational Autoencoders (VAE) in PyTorch.

  Language:Python6.7k1.1k

• microsoft/Semi-supervised-learning

  A Unified Semi-Supervised Learning Codebase (NeurIPS'22)

  Language:Python1.4k182

• bplank/semi-supervised-baselines

  Code for "Strong Baselines for Neural Semi-supervised Learning under Domain Shift" (Ruder & Plank, 2018 ACL)

  Language:Python6012

• ZuchniakK/MTKD

  Multi-Teacher Knowledge Distillation, code for my PhD dissertation. I used knowledge distillation as a decision-fusion and compressing mechanism for ensemble models.

  Language:Jupyter Notebook102

• Lavreniuk/Pseudo-labelling-and-knowledge-distillation-from-multiple-teachers

  Pseudo-labelling and knowledge distillation from multiple teachers for remote sensing monitoring of deforestation in Ukraine

  Language:Python12

• Rorozhl/MMKD

  This is the implementation for the ICME-2023 paper (Adaptive Multi-Teacher Knowledge Distillation with Meta-Learning).

  Language:Python193

• konyul/multi-task-mean-teacher

  multi task learning with pseudo labels created by teacher

  Language:Python21

• Rorozhl/CA-MKD

  This is the implementation for the ICASSP-2022 paper (Confidence-Aware Multi-Teacher Knowledge Distillation).

  Language:Python513

• ksaito-ut/atda

  Implemenation of Asymmetric-TriTraining by Tensorflow

  Language:Python246

• pytorch/captum

  Model interpretability and understanding for PyTorch

  Language:Python4.9k499

• ashutosh1919/explainable-cnn

  📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

  Language:Python21931