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Found 6 papers, 2 papers with code
Growing a Brain with Sparsity-Inducing Generation for Continual Learning
The base network learns knowledge of sequential tasks, and the sparsity-inducing hypernetwork generates parameters for each time step for evolving old knowledge.
Helpful or Harmful: Inter-Task Association in Continual Learning
In this work, we propose a novel approach to differentiate helpful and harmful information for old tasks using a model search to learn a current task effectively.
Ranked #1 on
Continual Learning
on Split MNIST (5 tasks)
Deep Elastic Networks with Model Selection for Multi-Task Learning
To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks.
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning.
Elastic-Net Regularization of Singular Values for Robust Subspace Learning
The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.
