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Found 5 papers, 2 papers with code
Revisiting Supervision for Continual Representation Learning
In the field of continual learning, models are designed to learn tasks one after the other.
Generalized Continual Category Discovery
However, these settings are not well aligned with real-life scenarios, where a learning agent has access to a vast amount of unlabeled data encompassing both novel (entirely unlabeled) classes and examples from known classes.
Logarithmic Continual Learning
Our approach leverages allocation of past data in a~set of generative models such that most of them do not require retraining after a~task.
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space.
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
We introduce a binary latent space autoencoder architecture to rehearse training samples for the continual learning of neural networks.
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