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Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Vishnu Sarukkai, Anirudh Jain, Burak Uzkent +1
Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground in…
Efficient Object Detection in Large Images using Deep Reinforcement Learning
Burak Uzkent, Christopher Yeh, Stefano Ermon
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images. However, in so…
Approximating Human Judgment of Generated Image Quality
Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao +2
Generative models have made immense progress in recent years, particularly in their ability to generate high quality images. However, that quality has been difficult to evaluate ri…
Approximating the Permanent by Sampling from Adaptive Partitions
Jonathan Kuck, Tri Dao, Hamid Rezatofighi +2
Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is…
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu, Tianhe Yu, Chelsea Finn +1
Providing a suitable reward function to reinforcement learning can be difficult in many real world applications. While inverse reinforcement learning (IRL) holds promise for automa…
Unsupervised Out-of-Distribution Detection with Batch Normalization
Jiaming Song, Yang Song, Stefano Ermon
Likelihood from a generative model is a natural statistic for detecting out-of-distribution (OoD) samples. However, generative models have been shown to assign higher likelihood to…