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Object Recognition in Deep Convolutional Neural Networks is Fundamentally Different to That in Humans
Object recognition is a primary function of the human visual system. It ...
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Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
A central problem in cognitive science and behavioural neuroscience as w...
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Comparing deep neural networks against humans: object recognition when the signal gets weaker
Human visual object recognition is typically rapid and seemingly effortl...
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Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neura...
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One-Shot Concept Learning by Simulating Evolutionary Instinct Development
Object recognition has become a crucial part of machine learning and com...
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Human-like Clustering with Deep Convolutional Neural Networks
Classification and clustering have been studied separately in machine le...
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The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks
Attention in machine learning is largely bottom-up, whereas people also ...
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Seeing Eye-to-Eye? A Comparison of Object Recognition Performance in Humans and Deep Convolutional Neural Networks under Image Manipulation
For a considerable time, deep convolutional neural networks (DCNNs) have reached human benchmark performance in object recognition. On that account, computational neuroscience and the field of machine learning have started to attribute numerous similarities and differences to artificial and biological vision. This study aims towards a behavioral comparison of visual core object recognition between humans and feedforward neural networks in a classification learning paradigm on an ImageNet data set. For this purpose, human participants (n = 65) competed in an online experiment against different feedforward DCNNs. The designed approach based on a typical learning process of seven different monkey categories included a training and validation phase with natural examples, as well as a testing phase with novel shape and color manipulations. Analyses of accuracy revealed that humans not only outperform DCNNs on all conditions, but also display significantly greater robustness towards shape and most notably color alterations. Furthermore, a precise examination of behavioral patterns highlights these findings by revealing independent classification errors between the groups. The obtained results endorse an implementation of recurrent circuits similar to the primate ventral stream in artificial vision models as a way to achieve adequate object generalization abilities across unexperienced manipulations.
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