In most machine learning tasks unambiguous ground truth labels can easily be acquired.
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
We introduce a new method for interpreting computer vision models: visually perceptible, decision-boundary crossing transformations.
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain.
Ranked #1 on Domain Adaptation on Synth Objects-to-LINEMOD
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
Training conditional maximum entropy models on massive data requires significant time and computational resources.