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Found 4 papers, 1 papers with code
Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping
Additionally, current approaches that deal with forgetting ignore the problem of catastrophic remembering, i. e. the worsening ability to discriminate between data from different tasks.
Ranked #1 on
Continual Learning
on ImageNet-50 (5 tasks)
Adaptive Neural Connections for Sparsity Learning
We propose Adaptive Neural Connections (ANC), a method for explicitly parameterizing fine-grained neuron-to-neuron connections via adjacency matrices at each layer that are learned through backpropagation.
Ranked #1 on
Sparse Learning
on ImageNet32
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
A longstanding problem for Deep Neural Networks (DNNs) is understanding their puzzling ability to generalize well.
Compressing GANs using Knowledge Distillation
From our experiments, we observe a qualitative limit for GAN's compression.
