45 citations · 89 across the 6 of their papers we have counts for
6 papers
Differentiable Top-k Operator with Optimal Transport
Yujia Xie, Hanjun Dai, Minshuo Chen +5
The top-k operation, i.e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, m…
Distance-Based Learning from Errors for Confidence Calibration
Chen Xing, Sercan Arik, Zizhao Zhang +1
Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways. To improve confidence calibration of DNNs, we propose a novel training method, distance-based l…
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels
Yucen Luo, Jun Zhu, Tomas Pfister
Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance. To mitigate this issue, we provid…
Data Valuation using Reinforcement Learning
Jinsung Yoon, Sercan O. Arik, Tomas Pfister
Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) doma…
Inserting Videos into Videos
Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang
In this paper, we introduce a new problem of manipulating a given video by inserting other videos into it. Our main task is, given an object video and a scene video, to insert the…
Harmonic Unpaired Image-to-image Translation
Rui Zhang, Tomas Pfister, Jia Li
The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, bu…