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- Stanford UniversityUS33 papers
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29 papers · 2 filters
Controllable and Progressive Image Extrapolation
Yijun Li, Lu Jiang, Ming-Hsuan Yang
Image extrapolation aims at expanding the narrow field of view of a given image patch. Existing models mainly deal with natural scene images of homogeneous regions and have no cont…
cFineGAN: Unsupervised multi-conditional fine-grained image generation
Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves…
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
Nikaash Puri, Sukriti Verma, Piyush Gupta +4
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by hi…
CNN-generated images are surprisingly easy to spot... for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang +2
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.…
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Jieru Mei, Yingwei Li, Xiaochen Lian +4
Search space design is very critical to neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that i…
Basis Prediction Networks for Effective Burst Denoising with Large Kernels
Zhihao Xia, Federico Perazzi, Michaël Gharbi +2
Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis eleme…