activity
20192022
most citedHow Does Learning Rate Decay Help Modern Neural Networks?

162 citations · 199 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation

Weihua He, Kaichao You, Zhendong Qiao +6

Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted s…

cs.LG2022

From Big to Small: Adaptive Learning to Partial-Set Domains

Zhangjie Cao, Kaichao You, Ziyang Zhang +2

Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirem…

cs.LG2021

LogME: Practical Assessment of Pre-trained Models for Transfer Learning

Kaichao You, Yong Liu, Jianmin Wang +1

This paper studies task adaptive pre-trained model selection, an underexplored problem of assessing pre-trained models for the target task and select best ones from the model zoo \…

cs.LG2019162 cited

How Does Learning Rate Decay Help Modern Neural Networks?

Kaichao You, Mingsheng Long, Jianmin Wang +1

Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empi…

cs.CV201936 cited

Learning to Transfer Examples for Partial Domain Adaptation

Zhangjie Cao, Kaichao You, Mingsheng Long +2

Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that eff…