CS332 (Advanced Topics in RL) Final Project, Autumn 2017
Fool me once, shame on me. Fool me twice, maybe you're using an adversarial discriminator<--autoencoder-->generator formulation to generate spam that looks a lot less like spam!
Technical details and experimental results in this paper:
- DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation: https://arxiv.org/pdf/1712.05419.pdf
Huge thanks to:
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Adversarial Learning for Neural Dialogue Generation: https://arxiv.org/pdf/1701.06547.pdf This policy gradient GAN-like formulation for text example generation borrows heavily from this paper.
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The Seq2Seq Autoencoder implemented by MaximumEntropy: https://github.com/MaximumEntropy/Seq2Seq-PyTorch