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Deep Neural Networks for Image-Based Dietary Assessment

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Implementation-Independent Representation for Deep Convolutional Neural Networks and Humans in Processing Faces.

Yiying Song¹, Yukun Qu², Shan Xu¹

¹Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, China.

Frontiers in Computational Neuroscience

|February 12, 2021

Summary

Deep convolutional neural networks (DCNNs) can match human performance, but do they think alike? This study reveals that a trained DCNN (VGG-Face) and humans use similar visual information for face gender classification, unlike a differently trained DCNN (AlexNet).

Keywords:

deep convolutional neural network face recognition face representation reverse correlation analysis visual intelligence

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Area of Science:

Computer Vision
Cognitive Science
Artificial Intelligence

Background:

Deep convolutional neural networks (DCNNs) demonstrate human-level performance on complex tasks.
The cognitive processes underlying DCNN performance remain largely unexplored compared to human cognition.

Purpose of the Study:

To investigate whether DCNNs utilize human-like processes for face gender classification.
To compare the representational strategies of DCNNs and humans in visual perception tasks.

Main Methods:

Applied a reverse-correlation method to visualize and compare internal representations of DCNNs and humans.
Utilized VGG-Face and AlexNet, DCNNs with distinct pre-training objectives (face identification vs. object categorization).

Main Results:

Humans and VGG-Face DCNN showed representational similarity in face gender classification, relying on low spatial frequencies.
AlexNet, despite successful gender classification, employed a different representation, highlighting the role of prior task experience.
Prior experience processing faces at a subordinate level (identification) was crucial for representational similarity.

Conclusions:

DCNNs can achieve human-level performance using implementation-independent representations similar to humans.
Task-specific pre-training significantly influences the representational strategies of DCNNs, impacting their similarity to human cognition.
Computational goals can be achieved through similar representations regardless of underlying hardware differences between DCNNs and biological systems.