Author: Xuehai He; Xingyi Yang; Shanghang Zhang; Jinyu Zhao; Yichen Zhang; Eric Xing; Pengtao Xie
Title: Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans Document date: 2020_4_17
ID: l3f469ht_58
Snippet: 1) Implementation Details: For classifiers trained from scratch, the Adam [56] optimizer is used with an initial learning rate of 0.0001 and a mini-batch size of 16. The cosine annealing scheduler is applied on the optimizer with a period of 10 to adjust the learning rate across the training process. We train our models with 50 epochs. We initialize the weights with Kaiming Initialization [57] ......
Document: 1) Implementation Details: For classifiers trained from scratch, the Adam [56] optimizer is used with an initial learning rate of 0.0001 and a mini-batch size of 16. The cosine annealing scheduler is applied on the optimizer with a period of 10 to adjust the learning rate across the training process. We train our models with 50 epochs. We initialize the weights with Kaiming Initialization [57] .
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