DEFT: distilling entangled factors by preventing information diffusion

نویسندگان

چکیده

Disentanglement is a highly desirable property of representation owing to its similarity human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks (IB). Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order provide an insight into problem, we develop annealing test calculate freezing point (IFP), which transition state freeze latent variables. We also explore these clues or inductive biases for separating entangled factors according differences in IFP distributions. found existing approaches suffer from diffusion increased diffuses all Based on this insight, propose novel framework, termed distilling factor (DEFT), address problem by scaling backward information. DEFT applies multistage training strategy, including multigroup encoders with different learning rates piecewise pressure, disentangle stage stage. evaluate three variants dSprite SmallNORB, show low-variance high-level scores. Furthermore, experiment under correlative shows incapable TC-based approaches. competitive performance unsupervised setting.

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ژورنال

عنوان ژورنال: Machine Learning

سال: 2022

ISSN: ['0885-6125', '1573-0565']

DOI: https://doi.org/10.1007/s10994-022-06134-7