mcj's repositories
VI-Non-IID
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling
balanced-joint-maximum-mean-discrepancy
Balanced joint maximum mean discrepancy for deep transfer learning
python_segy
A python toolbox for generating seismic training samples, integrated with pytorch
adversarial
Code and hyperparameters for the paper "Generative Adversarial Networks"
benchmark_results
Visual Tracking Paper List
bob.learn.linear
Linear Machine and Trainers for Bob - Mirrored from https://gitlab.idiap.ch/bob/bob.learn.linear
Boosting_DA
Boosting for Domain Adaptation
caffe
Caffe: a fast open framework for deep learning.
Cross-Domain-Landmarks-Selection-CDLS-
Released MATLAB code for CVPR_2016
DDUL
A test package including a test dataset and the trained model
Efficient-3DCNNs
PyTorch Implementation of "Resource Efficient 3D Convolutional Neural Networks", codes and pretrained models.
GFN-dehazing
Gated Fusion Network for Single Image Dehazing
kbtl
Kernelized Bayesian Transfer Learning
kernel_transfer
Kernel Transfer Learning
lihang_book_algorithm
致力于将李航博士《统计学习方法》一书中所有算法实现一遍
models
Models and examples built with TensorFlow
OfficeCaltechDomainAdaptation
Code, Images and Features for the Domain Adaptation benchmark dataset Office-Caltech
Pyortho
The Python version of local orthogonalization algorithm (Chen and Fomel, 2015, Random noise attenuation using local signal-and-noise orthogonalization, Geophysics, WD1-WD9).
pyseistr
Pyseistr is a python package for structural denoising and interpolation of multi-channel seismic data.
pytorch-nested-unet
PyTorch implementation of UNet++ (Nested U-Net).
SCL
Implementation of Structural Correspondence Learning
transfer
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习