TY - JOUR
T1 - Implicit visual learning
T2 - Image recognition via dissipative learning model
AU - Liu, Yan
AU - LIU, Yang
AU - Zhong, Shenghua
AU - Wu, Songtao
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/12
Y1 - 2016/12
N2 - According to consciousness involvement, human's learning can be roughly classified into explicit learning and implicit learning. Contrasting strongly to explicit learning with clear targets and rules, such as our school study of mathematics, learning is implicit when we acquire new information without intending to do so. Research from psychology indicates that implicit learning is ubiquitous in our daily life. Moreover, implicit learning plays an important role in human visual perception. But in the past 60 years, most of the well-known machine-learning models aimed to simulate explicit learning while the work of modeling implicit learning was relatively limited, especially for computer vision applications. This article proposes a novel unsupervised computational model for implicit visual learning by exploring dissipative system, which provides a unifying macroscopic theory to connect biology with physics. We test the proposed Dissipative Implicit Learning Model (DILM) on various datasets. The experiments show that DILM not only provides a good match to human behavior but also improves the explicit machine-learning performance obviously on image classification tasks.
AB - According to consciousness involvement, human's learning can be roughly classified into explicit learning and implicit learning. Contrasting strongly to explicit learning with clear targets and rules, such as our school study of mathematics, learning is implicit when we acquire new information without intending to do so. Research from psychology indicates that implicit learning is ubiquitous in our daily life. Moreover, implicit learning plays an important role in human visual perception. But in the past 60 years, most of the well-known machine-learning models aimed to simulate explicit learning while the work of modeling implicit learning was relatively limited, especially for computer vision applications. This article proposes a novel unsupervised computational model for implicit visual learning by exploring dissipative system, which provides a unifying macroscopic theory to connect biology with physics. We test the proposed Dissipative Implicit Learning Model (DILM) on various datasets. The experiments show that DILM not only provides a good match to human behavior but also improves the explicit machine-learning performance obviously on image classification tasks.
KW - Dissipative implicit learning model
KW - Dissipative theory
KW - Image recognition
KW - Implicit learning
KW - Visual data analysis
UR - http://www.scopus.com/inward/record.url?scp=85011394732&partnerID=8YFLogxK
U2 - 10.1145/2974024
DO - 10.1145/2974024
M3 - Journal article
AN - SCOPUS:85011394732
SN - 2157-6904
VL - 8
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
IS - 2
M1 - 31
ER -