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cs.CV2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning
Hongjun Wang, Guanbin Li, Xiaobai Liu +1
Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that de…
cs.CV2020
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
Xingyuan Chen, Ping Cai, Peng Jin +3
The autoregressive language model (ALM) trained with maximum likelihood estimation (MLE) is widely used in unconditional text generation. Due to exposure bias, the generated texts…
cs.CV2019
The Detection of Distributional Discrepancy for Text Generation
Xingyuan Chen, Ping Cai, Peng Jin +4
The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to allev…