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AdaptSegNet - Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
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).
deep-learning computer-vision domain-adaptation semantic-segmentation generative-adversarial-network adversarial-learning pytorchNeuralDialog-ZSDG - PyTorch codebase for zero-shot dialog generation, It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
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.
zero-shot-learning end-to-end-machine-learning neural-dialogue-agents domain-adaptation dialog sigdial-2018chainer-ADDA - Adversarial Discriminative Domain Adaptation in Chainer
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.
adda adversarial domain-adaptation chainer chainer-adda mnist svhndeep-transfer-learning - A collection of implementations of deep domain adaptation algorithms
Note that the results without this comes from paper. The results with this are run by myself with the code.
pytorch deep-transfer-learning domain-adaptation
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