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Basic Level Categorization Facilitates Visual Object Recognition
Recent advances in deep learning have led to significant progress in the...
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Can We Teach Computers to Understand Art? Domain Adaptation for Enhancing Deep Networks Capacity to De-Abstract Art
Humans comprehend a natural scene at a single glance; painters and other...
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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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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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Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning
Cinemagraphs are a compelling way to convey dynamic aspects of a scene. ...
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What does it mean to understand a neural network?
We can define a neural network that can learn to recognize objects in le...
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3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable Networks
Service robots, in general, have to work independently and adapt to the ...
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Sensory Optimization: Neural Networks as a Model for Understanding and Creating Art
This article is about the cognitive science of visual art. Artists create physical artifacts (such as sculptures or paintings) which depict people, objects, and events. These depictions are usually stylized rather than photo-realistic. How is it that humans are able to understand and create stylized representations? Does this ability depend on general cognitive capacities or an evolutionary adaptation for art? What role is played by learning and culture? Machine Learning can shed light on these questions. It's possible to train convolutional neural networks (CNNs) to recognize objects without training them on any visual art. If such CNNs can generalize to visual art (by creating and understanding stylized representations), then CNNs provide a model for how humans could understand art without innate adaptations or cultural learning. I argue that Deep Dream and Style Transfer show that CNNs can create a basic form of visual art, and that humans could create art by similar processes. This suggests that artists make art by optimizing for effects on the human object-recognition system. Physical artifacts are optimized to evoke real-world objects for this system (e.g. to evoke people or landscapes) and to serve as superstimuli for this system.
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