CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Self-Paced Learning: An Implicit Regularization Perspective
Yanbo Fan1,4; Ran He1,2,3,4; Jian Liang1,2,4; Baogang Hu1,4
Conference NameAmerican Association for AI National Conference(AAAI)
Conference Date2017
Conference PlaceSan Francisco, California USA
Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting strategy that is determined by the minimizer function. Existing
methods usually pursue this by artificially designing the explicit form of SPL regularizer. In this paper, we study a group of new regularizer (named self-paced implicit regularizer)
that is deduced from robust loss function. Based on the convex conjugacy theory, the minimizer function for selfpaced implicit regularizer can be directly learned from the
latent loss function, while the analytic form of the regularizer can be even unknown. A general framework (named SPL-IR) for SPL is developed accordingly. We demonstrate that the learning procedure of SPL-IR is associated with latent robust loss functions, thus can provide some theoretical insights for its working mechanism. We further analyze the relation between SPL-IR and half-quadratic optimization and provide a group of self-paced implicit regularizer. Finally, we implement SPL-IR to both supervised and unsupervised asks, and experimental results corroborate our ideas and demonstrate the correctness and effectiveness of implicit regularizers.
Indexed ByEI
Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, CASIA
2.Center for Research on Intelligent Perception and Computing, CASIA
3.Center for Excellence in Brain Science and Intelligence Technology, CAS
4.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yanbo Fan,Ran He,Jian Liang,et al. Self-Paced Learning: An Implicit Regularization Perspective[C],2017.
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