Learning 4 - Adversarial Methods for Text (12/1/2022)
Content:
- (Generative) Adversarial Networks
- Where to use the Adversary?: Features vs. Outputs
- GANs on Discrete Outputs
- Adversaries on Discrete Inputs
- Required Reading: GANs from Scratch (Mosquera 2018)
- Reference: Generative Adversarial Nets (Goodfellow et al. 2014)
- Reference: Example of Fuzzy Outputs (Lotter et al. 2015)
- Reference: Improved Techniques for Training GANs (Salimans et al. 2016)
- Reference: SeqGan (Yu et al. 2016)
- Reference: MT w/ GAN (Yang et al. 2017)
- Reference: MT w/ GAN (Wu et al. 2017)
- Reference: MT w/ Gumbel-Greedy Decoding (Gu et al. 2017)
- Reference: Dialog w/ GAN (Li et al. 2017)
- Reference: Perturbing Embeddings (Miyato et al. 2016)
- Reference: Adversarial Feature Learning for Domain Adaptation (Ganin et al. 2016)
- Reference: Adversarial Feature Learning for Bilingual Classification (Chen et al. 2016)
- Reference: Adversarial Feature Learning for Multilingual MT (Xie et al. 2017)
- Reference: Adversarial Feature Learning for Multi-task Classification (Liu et al. 2017)
- Reference: Adversarial Adaptation using Synthetic Data (Kim et al. 2017)
- Reference: Adversarial Feature Learning for Implicit Relation Classification (Qin et al. 2017)
- Reference: Professor Forcing (Lamb et al. 2016)
- Reference: Unsupervised Style Transfer for Text (Shen et al. 2017)
- Reference: PixelCNN++ (Salimans et al. 2017)
- Reference: Unrolled GAN (Metz et al. 2016)
- Reference: Unsupervised MT (Lample et al. 2017)
- Reference: Adversarial Generation of Natural Language (Rajeswar et al. 2017)
- Reference: WGAN (Arjovsky et al., 2017)
- Reference: Improved WGAN (Gulrajani et al., 2017)
- Reference: Controlled Text Generation (Hu et al., 2017)
- Reference: Dialog w/ GAN (Li et al., 2017)
- Reference: Unsupervised MT (Artetxe et al., 2017)
- Reference: Dual learning (He et al., 2016)
- Reference: Cycle GAN (Zhu et al., 2017)
- Reference: Gradient-Based Regularization (Roth et al., 2017)
- Reference: Unsupervised Embedding Alignment (Lample et al. 2018)
- Reference: Synthetic and Natural Noise Break NMT (Belinkov et al. 2018)
- Reference: Adversarial Noise for NMT (Ebrahimi et al. 2018)
- Reference: Evaluating Adversarial Noise for NMT (Michel et al. 2019)
- Reference: Adversarial ML Tutorial (Kolter and Madry 2019)
Slides: Adversarial Learning Slides