空白
计算机科学
序列(生物学)
连接主义
人工智能
班级(哲学)
模式识别(心理学)
过程(计算)
嵌入
功能(生物学)
超球体
人工神经网络
机械工程
遗传学
进化生物学
工程类
生物
操作系统
作者
Yuecong Min,Peiqi Jiao,Yanan Li,Xiaotao Wang,Lei Lei,Xiujuan Chai,Xilin Chen
标识
DOI:10.1007/978-3-031-20068-7_14
摘要
Connectionist Temporal Classification (CTC) is a popular objective function in sequence recognition, which provides supervision for unsegmented sequence data through aligning sequence and its corresponding labeling iteratively. The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC. In this study, we propose an objective function named RadialCTC that constrains sequence features on a hypersphere while retaining the iterative alignment mechanism of CTC. The learned features of each non-blank class are distributed on a radial arc from the center of the blank class, which provides a clear geometric interpretation and makes the alignment process more efficient. Besides, RadialCTC can control the peaky behavior by simply modifying the logit of the blank class. Experimental results of recognition and localization demonstrate the effectiveness of RadialCTC on two sequence recognition applications.
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