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우리는 이중 하강 현상이 CNN, ResNet, 트랜스포머에서 발생한다는 것을 보여준다: 성능이 먼저 향상되었다가 나빠졌다가 모델 크기, 데이터 크기가 증가함에 따라 다시 개선된다는 점이다...
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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.
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