mengchuangji Goto Github PK

adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

amazingtransferlearning

balanced-joint-maximum-mean-discrepancy

Balanced joint maximum mean discrepancy for deep transfer learning

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

da-mm

Deep transfer learning method based on automatic Domain Alignment and Moment Matching

ddae-rcab

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.

gan-intro

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

liresrc

liresrc

machine-learning

machine-learning-matlab

some algorithm in machine learning using matlab

models

Models and examples built with TensorFlow

officecaltechdomainadaptation

Code, Images and Features for the Domain Adaptation benchmark dataset Office-Caltech

pctkvm

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.

python_segy

A python toolbox for generating seismic training samples, integrated with pytorch

pytorch-nested-unet

PyTorch implementation of UNet++ (Nested U-Net).

scl

Implementation of Structural Correspondence Learning

siammask

[CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach

transfer

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks