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The study explores face pareidolia, where humans see faces in random stimuli, using the DeepGaze model to compare its detection abilities with human gaze patterns, revealing DeepGaze’s potential in recognizing face-like patterns but also its limitations in fully capturing the nuances of human gaze behavior in face pareidolia.
Published:
The poster presents a study where task-optimized convolutional neural networks (CNNs) challenge the expertise hypothesis, suggesting that systems broadly optimized for object recognition provide a better foundation for learning fine-grained tasks like car discrimination than systems optimized for face recognition, thus questioning the computational viability of the expertise hypothesis.