Invertible Monotone Operators for Normalizing Flows
–Neural Information Processing Systems
Since the architecture of ResNet-based normalizing flows is more flexible than that of coupling-based models, ResNet-based normalizing flows have been widely studied in recent years. Despite their architectural flexibility, it is well-known that the current ResNet-based models suffer from constrained Lipschitz constants. In this paper, we propose the monotone formulation to overcome the issue of the Lipschitz constants using monotone operators and provide an in-depth theoretical analysis. Furthermore, we construct an activation function called Concatenated Pila (CPila) to improve gradient flow.
Neural Information Processing Systems
Aug-15-2025, 15:13:11 GMT
- Country:
- Europe > Italy > Calabria > Catanzaro Province > Catanzaro (0.04)
- Genre:
- Research Report (0.46)
- Technology: