RanDumb: a simple approach that questions the efficacy of continual representation learning
<p>We propose RanDumb to examine the efficacy of continual representation learning. RanDumb embeds raw pixels using a fixed random transform which approximates an RBF-Kernel, initialized before seeing any data, and learns a simple linear classifier on top. We present a surprising and consisten...
| Główni autorzy: | , , , , , |
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| Format: | Conference item |
| Język: | English |
| Wydane: |
Neural Information Processing Systems
2025
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