Online Adaptative Curriculum Learning for GANs

By Thang Van Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm. 2018

43 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Generative Models

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Citations in the corpus

A Style-Based Generator Architecture for Generative Adversarial Networks (2018, methodology): “, using multiple discriminators [17, 43, 10], multiresolution discrimination [55, 51], or self-attention [57].”
Projected GANs Converge Faster (2021, background): “A different line of work considers a setup with multiple discriminators, applied to either the generated RGB image [8, 13] or low-dimensional projections thereof [1, 40].”
A U-Net Based Discriminator for Generative Adversarial Networks (2020, background): “Other lines of work focused mainly on improving the discriminator by exploiting multiple [36, 13, 11] and multi-resolution [45, 42] discriminators, using spatial feedback of the discriminator [17], an auto-encoder architecture with the reconstructionbased feedback to the generator [52] or self-super”
Video Summarization Using Deep Neural Networks: A Survey (2021, background): “, [108], [109], [110]), to extend the learning boundaries of unsupervised video summarization.”
StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis (2021, background): “(2014), GANs have demonstrated high-quality results (Durugkar et al., 2017; Mordido et al., 2018; Doan et al., 2019; Zhang et al., 2019; Brock et al., 2018; Karras et al., 2018).”
Multi-objective training of Generative Adversarial Networks with multiple discriminators (2019, background): “Prior work, including (Durugkar et al., 2016; Doan et al., 2018) have also explored training with multiple discriminators.”