Transferring Knowledge across Learning Processes

Sebastian Flennerhag; Pablo Garcia Moreno; Neil D. Lawrence; Andreas Damianou

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Transferring Knowledge across Learning Processes

Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas Damianou

International Conference on Learning Representations, 2019.

Abstract

In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.

Links

Cite this Paper

BibTeX


@InProceedings{Flennerhag-transferring19,
  title = 	 {Transferring Knowledge across Learning Processes},
  author = 	 {Flennerhag, Sebastian and Garcia Moreno, Pablo and Lawrence, Neil D. and Damianou, Andreas},
  booktitle = 	 {International Conference on Learning Representations},
  year = 	 {2019},
  pdf = 	 {https://openreview.net/attachment?id=Hkg-xgrYvH&name=original_pdf},
  url = 	 {/publications/transferring-knowledge-across-learning-processes.html},
  abstract = 	 {In complex transfer learning scenarios new tasks might not be
tightly linked to previous tasks. Approaches that transfer
information contained only in the final parameters of a source model
will therefore struggle. Instead, transfer learning at at higher
level of abstraction is needed. We propose Leap, a framework that
achieves this by transferring knowledge across learning
processes. We associate each task with a manifold on which the
training process travels from initialization to final parameters and
construct a meta-learning objective that minimizes the expected
length of this path. Our framework leverages only information
obtained during training and can be computed on the fly at
negligible cost. We demonstrate that our framework outperforms
competing methods, both in meta-learning and transfer learning, on a
set of computer vision tasks. Finally, we demonstrate that Leap can
transfer knowledge across learning processes in demanding
reinforcement learning environments (Atari) that involve millions of
gradient steps.
}
}

Endnote

%0 Conference Paper
%T Transferring Knowledge across Learning Processes
%A Sebastian Flennerhag
%A Pablo Garcia Moreno
%A Neil D. Lawrence
%A Andreas Damianou
%B International Conference on Learning Representations
%D 2019	
%F Flennerhag-transferring19
%U /publications/transferring-knowledge-across-learning-processes.html
%X In complex transfer learning scenarios new tasks might not be
tightly linked to previous tasks. Approaches that transfer
information contained only in the final parameters of a source model
will therefore struggle. Instead, transfer learning at at higher
level of abstraction is needed. We propose Leap, a framework that
achieves this by transferring knowledge across learning
processes. We associate each task with a manifold on which the
training process travels from initialization to final parameters and
construct a meta-learning objective that minimizes the expected
length of this path. Our framework leverages only information
obtained during training and can be computed on the fly at
negligible cost. We demonstrate that our framework outperforms
competing methods, both in meta-learning and transfer learning, on a
set of computer vision tasks. Finally, we demonstrate that Leap can
transfer knowledge across learning processes in demanding
reinforcement learning environments (Atari) that involve millions of
gradient steps.

RIS


TY  - CPAPER
TI  - Transferring Knowledge across Learning Processes
AU  - Sebastian Flennerhag
AU  - Pablo Garcia Moreno
AU  - Neil D. Lawrence
AU  - Andreas Damianou
BT  - International Conference on Learning Representations
DA  - 2019/04/26	
ID  - Flennerhag-transferring19
L1  - https://openreview.net/attachment?id=Hkg-xgrYvH&name=original_pdf
UR  - /publications/transferring-knowledge-across-learning-processes.html
AB  - In complex transfer learning scenarios new tasks might not be
tightly linked to previous tasks. Approaches that transfer
information contained only in the final parameters of a source model
will therefore struggle. Instead, transfer learning at at higher
level of abstraction is needed. We propose Leap, a framework that
achieves this by transferring knowledge across learning
processes. We associate each task with a manifold on which the
training process travels from initialization to final parameters and
construct a meta-learning objective that minimizes the expected
length of this path. Our framework leverages only information
obtained during training and can be computed on the fly at
negligible cost. We demonstrate that our framework outperforms
competing methods, both in meta-learning and transfer learning, on a
set of computer vision tasks. Finally, we demonstrate that Leap can
transfer knowledge across learning processes in demanding
reinforcement learning environments (Atari) that involve millions of
gradient steps.

ER  -

APA


Flennerhag, S., Garcia Moreno, P., Lawrence, N.D. & Damianou, A.. (2019). Transferring Knowledge across Learning Processes. International Conference on Learning Representations Available from /publications/transferring-knowledge-across-learning-processes.html.