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1
Intro
2
Adversarial Methods
3
generative adversarial networks
4
nonlatent models
5
ML vs GAN
6
Basic Paradigm
7
Loss Function
8
Distribution Matching
9
Distribution Matching Pseudocode
10
Why are Gans good
11
Image Generation
12
Problems
13
Classes
14
Discriminators
15
Questions
16
Discrete choices
17
Domain and variant representations
18
Language variant representations
19
Unsupervised style transfer
Description:
Explore adversarial learning in natural language processing through this advanced lecture from CMU's CS 11-711 course. Delve into generative adversarial networks, examining their applications in both feature and output spaces. Investigate the challenges of applying GANs to discrete outputs and learn about adversarial techniques for discrete inputs. Gain insights into distribution matching, image generation, and unsupervised style transfer in language processing. Enhance your understanding of advanced NLP concepts and their practical implementations in this comprehensive 81-minute session led by Graham Neubig.

CMU Advanced NLP 2021 - Adversarial Learning

Graham Neubig
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