271 citations · 603 across the 6 of their papers we have counts for
4 papers · 1 filter
Understanding disentangling in -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal +4
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we s…
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins, Arka Pal, Andrei A. Rusu +6
Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a s…
SCAN: Learning Hierarchical Compositional Visual Concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey +7
The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules…
Early Visual Concept Learning with Unsupervised Deep Learning
Irina Higgins, Loic Matthey, Xavier Glorot +5
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning…