Generative Adversarial Networks (with 7 co-authors)
2 korrents from this paper
In plain words
Two networks play a game where one creates fake samples to fool the other into thinking they are real data, until the creator matches the true data distribution. It lets generative models train with simple backpropagation without Markov chains or hard probability steps, and experiments show competitive samples on image sets. It proposes this adversarial training method for generative models, sidestepping the inference and sampling difficulties of earlier deep generative approaches.
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Near this, by wording
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 3 claims
- Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv)
- He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv)
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