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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

On-Line Adaptative Curriculum Learning for GANs

February 1, 2023

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Authors

Thang Doan

McGill University


João Monteiro

Institut National de la Recherche Scientifique


Isabela Albuquerque

Institut National de la Recherche Scientifique


Bogdan Mazoure

McGill University


Audrey Durand

McGill University


Joelle Pineau

McGill University


R. Devon Hjelm

Microsoft Research


DOI:

10.1609/aaai.v33i01.33013470


Abstract:

Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning. To this end, we propose a reward function which reflects the progress made by the generator and dynamically update the mixture weights allocated to each discriminator. We also draw connections between our algorithm and stochastic optimization methods and then show that existing approaches using multiple discriminators in literature can be recovered from our framework. We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support. On the other hand, highly expressive discriminators ensure samples quality. Finally, experimental results show that our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum. These results also support the claim that weaker discriminators have higher entropy improving modes coverage.

Topics: AAAI

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HOW TO CITE:

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm On-Line Adaptative Curriculum Learning for GANs Proceedings of the AAAI Conference on Artificial Intelligence (2019) 3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm On-Line Adaptative Curriculum Learning for GANs AAAI 2019, 3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm (2019). On-Line Adaptative Curriculum Learning for GANs. Proceedings of the AAAI Conference on Artificial Intelligence, 3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm. On-Line Adaptative Curriculum Learning for GANs. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm. 2019. On-Line Adaptative Curriculum Learning for GANs. "Proceedings of the AAAI Conference on Artificial Intelligence". 3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm. (2019) "On-Line Adaptative Curriculum Learning for GANs", Proceedings of the AAAI Conference on Artificial Intelligence, p.3470-3477

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm, "On-Line Adaptative Curriculum Learning for GANs", AAAI, p.3470-3477, 2019.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm. "On-Line Adaptative Curriculum Learning for GANs". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm. "On-Line Adaptative Curriculum Learning for GANs". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 3470-3477.

Thang Doan||João Monteiro||Isabela Albuquerque||Bogdan Mazoure||Audrey Durand||Joelle Pineau||R. Devon Hjelm. On-Line Adaptative Curriculum Learning for GANs. AAAI[Internet]. 2019[cited 2023]; 3470-3477.


ISSN: 2374-3468


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