Search Results for author:
Found 10 papers, 8 papers with code
Self-Supervised Dynamic Networks for Covariate Shift Robustness
As supervised learning still dominates most AI applications, test-time performance is often unexpected.
Dynamic-Net: Tuning the Objective Without Re-training for Synthesis Tasks
Our approach considers an "objective-space" as the space of all linear combinations of two objectives, and the Dynamic-Net is emulating the traversing of this objective-space at test-time, without any further training.
Adversarial Feedback Loop
In this paper we propose a novel method that makes an explicit use of the discriminator in test-time, in a feedback manner in order to improve the generator results.
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results.
The 2018 PIRM Challenge on Perceptual Image Super-resolution
This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018.
Ranked #33 on
Video Quality Assessment
on MSU SR-QA Dataset
Maintaining Natural Image Statistics with the Contextual Loss
Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images.
The Contextual Loss for Image Transformation with Non-Aligned Data
Feed-forward CNNs trained for image transformation problems rely on loss functions that measure the similarity between the generated image and a target image.
Photorealistic Style Transfer with Screened Poisson Equation
Recent work has shown impressive success in transferring painterly style to images.
Template Matching with Deformable Diversity Similarity
We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template.
Saliency Driven Image Manipulation
Have you ever taken a picture only to find out that an unimportant background object ended up being overly salient?
