Incremental learning with self-organizing maps

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How researchers see this paper

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CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning (2022, background): “Rehearsal with stored data [3–5, 7, 9, 10, 18, 19, 24, 29, 42, 51–53, 64, 68] or samples from a generative model [27, 28,48,56,60] is highly effective when storing training data or training/saving a generative model is possible.”
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (2021, methodology): “Experience replay with stored data [2, 3, 7, 8, 13, 17, 22, 27, 38, 44, 45, 46, 57] or samples from a generative model [25, 26, 43, 49, 54] is highly effective when storing training data or training/saving a generative model is possible, unlike the DFCIL setting.”
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer (2021, positive): “Rehearsal methods include storage to "replay" data or experiences from previous tasks to mitigate catastrophic forgetting [10, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39].”
A Closer Look at Rehearsal-Free Continual Learning * (2022, unknown): “Rehearsal with stored data [3–5,7, 8, 15, 16 , 22, 28, 39 , 47– 49, 59 ] or samples from a generative model [ 26, 27, 43,”