Deep double descent

归档编码:deep-double-descent-19b78ea1

核心要点速览

We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.

首发信源 OpenAI News OpenAI 官方博客、模型发布与科研进展
所属板块 人工智能 AIPEDIAS 实时情报监测
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