Displaying 1 to 8 from 8 results

transferlearning-tutorial - 《迁移学习简明手册》LaTex源码

  •    TeX

Jindong Wang et al. Transfer Learning Tutorial. 2018. 王晋东等. 迁移学习简明手册. 2018.

awesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc


A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA), but don't hesitate to suggest resources in other subfields of transfer learning. I accept pull requests. Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.

AdaptSegNet - Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

  •    Python

Pytorch implementation of our method for adapting semantic segmentation from the synthetic dataset (source domain) to the real dataset (target domain). Based on this implementation, our result is ranked 3rd in the VisDA Challenge. Learning to Adapt Structured Output Space for Semantic Segmentation Yi-Hsuan Tsai*, Wei-Chih Hung*, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang and Manmohan Chandraker IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (spotlight) (* indicates equal contribution).

NeuralDialog-ZSDG - PyTorch codebase for zero-shot dialog generation, It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

  •    Python

Codebase for Zero-Shot Dialog Generation with Cross-Domain Latent Actions, published as a long paper in SIGDIAL 2018. Reference information is in the end of this page. Presentation slides can be found here. This work won the best paper award at SIGDIAL 2018.

chainer-ADDA - Adversarial Discriminative Domain Adaptation in Chainer

  •    Python

Implementation of Adversarial Discriminative Domain Adaptation in Chainer. Note this code depends on this version of Chainer (or newer). Please check out the source from that link rather than installing via pip.

Learning-via-Translation - Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification (https://arxiv

  •    Matlab

The first step is to translate the annotated dataset from source domain to target domain in an unsupervised manner. For more reference, you can find our modified training code and generating code in ./SPGAN. We wrote a detailed README. If you still has some question, feel free to contact me (dengwj16@gmail.com).

deep-transfer-learning - A collection of implementations of deep domain adaptation algorithms

  •    Python

Note that the results without this comes from paper. The results with this are run by myself with the code.