Search results with tag "Adversarial training"
Generative Adversarial Imitation Learning - NeurIPS
proceedings.neurips.ccmodeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where we find that it outperforms competing methods by a wide margin in training policies for complex,
Generative Adversarial Networks arXiv:1809.00219v2 [cs.CV ...
arxiv.orgThroughout the literature, photo-realism is usually attained by adversarial training with GAN [15]. Recently there are a bunch of works that focus on de-veloping more e ective GAN frameworks. WGAN [31] proposes to minimize a reasonable and e cient approximation of Wasserstein distance and regularizes discriminator by weight clipping.
![arXiv:1411.1784v1 [cs.LG] 6 Nov 2014](/cache/preview/f/a/6/8/3/a/5/7/thumb-fa683a5739b507f70aa173f9c218eee1.jpg)
arXiv:1411.1784v1 [cs.LG] 6 Nov 2014
arxiv.orgthe adversarial training framework allows for considerable flexibility in how this hidden representa-tion is composed. 1 In the discriminator x and y are presented as inputs and to a discriminative function (embodied again by a MLP in this case).
Adversarial Examples and Adversarial Training
cs231n.stanford.eduMay 30, 2017 · Adversarial Training of other Models • Linear models: SVM / linear regression cannot learn a step function, so adversarial training is less useful, very similar to weight decay • k-NN: adversarial training is prone to overfitting. • Takeway: neural nets can actually become more secure than other models. Adversarially trained