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20192026
most citedSurrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data

12 citations · 12 across the 2 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models

Yuwei Ning, Liangzhi Wang, Yi Xiao +5

Realistic weather translation is valuable for developing and evaluating autonomous driving systems, yet collecting paired videos of the same scenes under different weather conditio…

cs.CV2023

Scaling Vision-based End-to-End Driving with Multi-View Attention Learning

Yi Xiao, Felipe Codevilla, Diego Porres +1

On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., st…

cs.CV2020

Action-Based Representation Learning for Autonomous Driving

Yi Xiao, Felipe Codevilla, Christopher Pal +1

Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creati…

cs.CV2019

Multimodal End-to-End Autonomous Driving

Yi Xiao, Felipe Codevilla, Akhil Gurram +2

A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressin…

cs.CV201912 cited

Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data

Nima Tajbakhsh, Yufei Hu, Junli Cao +6

We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underly…