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Found 6 papers, 3 papers with code
Expanding continual few-shot learning benchmarks to include recognition of specific instances
Continual learning and few-shot learning are important frontiers in progress towards broader Machine Learning (ML) capabilities.
One-shot learning for the long term: consolidation with an artificial hippocampal algorithm
The results demonstrated that with the addition of AHA, the system could learn in one-shot and consolidate the knowledge for the long term without catastrophic forgetting.
Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture
We propose an extension to the standard Omniglot classification-generalisation framework that additionally tests the ability to distinguish specific instances after one exposure and introduces noise and occlusion corruption.
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning
AHA is a biologically-plausible computational model of the Hippocampus.
Learning distant cause and effect using only local and immediate credit assignment
We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs.
Sparse Unsupervised Capsules Generalize Better
We show that unsupervised training of latent capsule layers using only the reconstruction loss, without masking to select the correct output class, causes a loss of equivariances and other desirable capsule qualities.
