Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate ...
This is the repository of the RSRGAN project. Our original paper can be found here. In this work we investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. Our RIRs were from here, and we used KALDI to simulate reverberant speech....
2014年Ian Goodfellow首次提出Generative adversarial networks (生成对抗网络)简称GANs,生成对抗网络就开始在计算机视觉领域得到广泛应用,成为对有用的视觉任务网络之一,也是如今计算机视觉热点研究领域之一,其已经出现的应用领域与方向如下: OpenCV学堂 2019/10/11 3.3K0 【GAN货】生成对抗网络知识资料全集(论文/代码/教程...
Boundary-Seeking Generative Adversarial Networks Authors R Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, Yoshua Bengio [Paper][Code] Run Example $ cd models/bgan/ $ python3.7 bgan.py Cluster GAN ClusterGAN: Latent Space Clustering in Generative Adversarial Networks Authors...
paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf github(Caffe): https://github.com/xiaolonw/adversarial-frcnn Faster R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arxiv: http://arxiv.org/abs/1506.01497 gitxiv: http://www.git...
4. star:4977|U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation(图像翻译/无监督) 论文:arxiv.org/pdf/1907.1083 代码:github.com/taki0112/UGA 5. star:2106|On the Variance of the Adaptive Learning Rate and Beyond 论文:ar...
7. star:1018|Generative Models for Effective ML on Private, Decentralized Datasets 论文:https://arxiv.org/pdf/1911.06679v2.pdf 代码:https://github.com/tensorflow/federated/tree/master/tensorflow_federated/python/research/gans 8. star:963|Behaviour Suite for Reinforcement Learning(强化学习) ...
http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf GANs来了。论文提出了一个通过对抗过程估计生成模型的新框架,在新框架中同时训练两个模型:一个用来捕获数据分布的生成模型G,和一个用来估计样本来自训练数据而不是G的概率的判别模型D,G的训练过程是最大化D产生错误的概率。在训练或生成样本期间...
RelGAN - RelGAN: Relational Generative Adversarial Networks for Text Generation https://openreview.net/forum?id=rJedV3R5tm 入门 开始 gitclone cdTextGAN-PyTorch 对于真实数据实验,可以从下载Image COCO和EMNLP新闻数据集,下载链接: https://drive.google.com/drive/folders/1XvT3GqbK1wh3XhTgqBLWUtH_mLzGn...
Boundary-Seeking Generative Adversarial Networks R Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, Yoshua Bengio https://arxiv.org/abs/1702.08431 CC-GAN 这种模型能用半监督学习的方法,修补图像上缺失的部分。 Code: