22 citations · 23 across the 3 of their papers we have counts for
4 papers
Disentangling Domain and Content
Dan Andrei Iliescu, Aliaksei Mikhailiuk, Damon Wischik +1
Many real-world datasets can be divided into groups according to certain salient features (e.g. grouping images by subject, grouping text by font, etc.). Often, machine learning ta…
Training a Task-Specific Image Reconstruction Loss
Aamir Mustafa, Aliaksei Mikhailiuk, Dan Andrei Iliescu +2
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution. The loss function should e…
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
Aliaksei Mikhailiuk, Maria Perez-Ortiz, Dingcheng Yue +2
Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of di…
Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization
Aliaksei Mikhailiuk, Clifford Wilmot, Maria Perez-Ortiz +2
Pairwise comparison data arise in many domains with subjective assessment experiments, for example in image and video quality assessment. In these experiments observers are asked t…