Implicit visual learning: Image recognition via dissipative learning model

Yan Liu, Yang LIU, Shenghua Zhong, Songtao Wu

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number31
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
Publication statusPublished - Dec 2016

Scopus Subject Areas

  • Theoretical Computer Science
  • Artificial Intelligence

User-Defined Keywords

  • Dissipative implicit learning model
  • Dissipative theory
  • Image recognition
  • Implicit learning
  • Visual data analysis


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