70,522 research outputs found
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks in a
sequential manner, where knowledge gained from previous tasks is retained and
used to aid future learning over the lifetime of the learner. It is essential
towards the development of intelligent machines that can adapt to their
surroundings. In this work we focus on a lifelong learning approach to
unsupervised generative modeling, where we continuously incorporate newly
observed distributions into a learned model. We do so through a student-teacher
Variational Autoencoder architecture which allows us to learn and preserve all
the distributions seen so far, without the need to retain the past data nor the
past models. Through the introduction of a novel cross-model regularizer,
inspired by a Bayesian update rule, the student model leverages the information
learned by the teacher, which acts as a probabilistic knowledge store. The
regularizer reduces the effect of catastrophic interference that appears when
we learn over sequences of distributions. We validate our model's performance
on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A
and demonstrate that our model mitigates the effects of catastrophic
interference faced by neural networks in sequential learning scenarios.Comment: 32 page
A PAC-Bayesian bound for Lifelong Learning
Transfer learning has received a lot of attention in the machine learning
community over the last years, and several effective algorithms have been
developed. However, relatively little is known about their theoretical
properties, especially in the setting of lifelong learning, where the goal is
to transfer information to tasks for which no data have been observed so far.
In this work we study lifelong learning from a theoretical perspective. Our
main result is a PAC-Bayesian generalization bound that offers a unified view
on existing paradigms for transfer learning, such as the transfer of parameters
or the transfer of low-dimensional representations. We also use the bound to
derive two principled lifelong learning algorithms, and we show that these
yield results comparable with existing methods.Comment: to appear at ICML 201
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