「Deep Learning」Note on Rethinking ImageNet Pre-training
作者：Kaiming He, Ross Girshick, Piotr Dollár
單位：Facebook AI Research (FAIR)
0 摘要
在物體檢測和例項分割中，以COCO資料集為研究物件，探索從隨機初始化情況中訓練標準模型，並且取得有競爭力的結果，這些結果 不差於(no worse than) 在ImageNet預訓練基礎上 微調 的模型結果。犧牲在於，訓練更多迭代。還探索這些結果在以下情況也成立：
(i) using only 10% of the training data;
(ii) for deeper and wider models;
(iii) for multiple tasks and metrics
ImageNet pre-training在訓練(微調)中會加速模型在早期中收斂，但是提高最終目標任務的準確率，和提供正則化，是不必要的。
1 介紹
[1] Rethinking ImageNet Pre-training 2018 [paper]
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