SOTAVerified

Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks

2024-11-01Code Available0· sign in to hype

Yuyan Zhang, Derya Soydaner, Fatemeh Behrad, Lisa Koßmann, Johan Wagemans

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Deep neural networks perform well in object recognition, but do they perceive objects like humans? This study investigates the Gestalt principle of closure in convolutional neural networks. We propose a protocol to identify closure and conduct experiments using simple visual stimuli with progressively removed edge sections. We evaluate well-known networks on their ability to classify incomplete polygons. Our findings reveal a performance degradation as the edge removal percentage increases, indicating that current models heavily rely on complete edge information for accurate classification. The data used in our study is available on Github.

Tasks

Reproductions