4 papers
MFSR: MeanFlow Distillation for One Step Real-World Image Super Resolution
Ruiqing Wang, Kai Zhang, Yuanzhi Zhu +3
Diffusion- and flow-based models have advanced Real-world Image Super-Resolution (Real-ISR), but their multi-step sampling makes inference slow and hard to deploy. One-step distill…
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
Yuanzhi Zhu, Ruiqing Wang, Shilin Lu +3
Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to tra…
A Trainable Optimizer
Ruiqi Wang, Diego Klabjan
The concept of learning to optimize involves utilizing a trainable optimization strategy rather than relying on manually defined full gradient estimations such as ADAM. We present…
Divergence Results and Convergence of a Variance Reduced Version of ADAM
Ruiqi Wang, Diego Klabjan
Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications…