Winning Lottery Tickets in Deep Generative Models
Keywords:Neural Generative Models & Autoencoders, (Deep) Neural Network Algorithms, Applications
AbstractThe lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network. Prior works in lottery tickets have primarily focused on the supervised learning setup, with several papers proposing effective ways of finding winning tickets in classification problems. In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs. We show that the popular iterative magnitude pruning approach (with late resetting) can be used with generative losses to find the winning tickets. This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets. We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models. Furthermore, we show the practical benefits of lottery tickets in generative models by detecting tickets at very early stages in training called early-bird tickets. Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints. These results out-perform existing early pruning methods like SNIP (Lee, Ajanthan, and Torr 2019) and GraSP(Wang, Zhang, and Grosse 2020). Our findings shed light towards existence of proper network initializations that could improve convergence and stability of generative models.
How to Cite
Kalibhat, N. M., Balaji, Y., & Feizi, S. (2021). Winning Lottery Tickets in Deep Generative Models. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 8038-8046. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16980
AAAI Technical Track on Machine Learning II