PathNet: Evolution Channels Gradient Descent in Super Neural Networks

For artificial general intelligence (AGI) it would be efficient
if multiple users trained the same giant neural network, permitting
parameter reuse, without catastrophic forgetting.
PathNet is a first step in this direction. It is a neural network
algorithm that uses agents embedded in the neural network
whose task is to discover which parts of the network to
re-use for new tasks. Agents are pathways (views) through
the network which determine the subset of parameters that
are used and updated by the forwards and backwards passes
of the backpropogation algorithm. During learning, a tournament
selection genetic algorithm is used to select pathways
through the neural network for replication and mutation.
Pathway fitness is the performance of that pathway
measured according to a cost function. We demonstrate
successful transfer learning; fixing the parameters along a
path learned on task A and re-evolving a new population
of paths for task B, allows task B to be learned faster than
it could be learned from scratch or after fine-tuning. Paths
evolved on task B re-use parts of the optimal path evolved
on task A. Positive transfer was demonstrated for binary
MNIST, CIFAR, and SVHN supervised learning classification
tasks, and a set of Atari and Labyrinth reinforcement
learning tasks, suggesting PathNets have general applicability
for neural network training. Finally, PathNet also significantly
improves the robustness to hyperparameter choices
of a parallel asynchronous reinforcement learning algorithm
(A3C).

Source: https://arxiv.org/pdf/1701.08734.pdf

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