Kavli Affiliate: Ke Wang
| First 5 Authors: Zhuowen Zheng, Yain-Whar Si, Xiaochen Yuan, Junwei Duan, Ke Wang
| Summary:
Nowadays, with the advancement of deep neural networks (DNNs) and the
availability of large-scale datasets, the face recognition (FR) model has
achieved exceptional performance. However, since the parameter magnitude of the
fully connected (FC) layer directly depends on the number of identities in the
dataset. If training the FR model on large-scale datasets, the size of the
model parameter will be excessively huge, leading to substantial demand for
computational resources, such as time and memory. This paper proposes the
attention fully connected (AttFC) layer, which could significantly reduce
computational resources. AttFC employs an attention loader to generate the
generative class center (GCC), and dynamically store the class center with
Dynamic Class Container (DCC). DCC only stores a small subset of all class
centers in FC, thus its parameter count is substantially less than the FC
layer. Also, training face recognition models on large-scale datasets with one
GPU often encounter out-of-memory (OOM) issues. AttFC overcomes this and
achieves comparable performance to state-of-the-art methods.
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