171 citations · 694 across the 32 of their papers we have counts for
6 papers · 1 filter
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Alexander Kolesnikov, Christoph H. Lampert
We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two n…
iCaRL: Incremental Classifier and Representation Learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl +1
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of…
Extrapolation and learning equations
Georg Martius, Christoph H. Lampert
In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mec…
Improving Weakly-Supervised Object Localization By Micro-Annotation
Alexander Kolesnikov, Christoph H. Lampert
Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a meth…
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
Alexander Kolesnikov, Christoph H. Lampert
We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to…
Multi-Task Learning with Labeled and Unlabeled Tasks
Anastasia Pentina, Christoph H. Lampert
In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training dat…