Which learning paradigm (e.g. supervised, self-supervised, unsupervised, reinforcement learning) do brains...
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In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to develop the visual skills to detect objects and make sense of the visual world around them. My educated guess is that brains must be performing some kind of self-supervision to train their visual neural networks. Does anybody know which learning paradigm are brains using to achieve such an outstanding level of visual competence? Are there any works on replicating this paradigm in computer vision?
machine-learning neural-network computer-vision
asked 7 mins ago
Pablo MessinaPablo Messina
435
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add a comment |
$begingroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to develop the visual skills to detect objects and make sense of the visual world around them. My educated guess is that brains must be performing some kind of self-supervision to train their visual neural networks. Does anybody know which learning paradigm are brains using to achieve such an outstanding level of visual competence? Are there any works on replicating this paradigm in computer vision?
machine-learning neural-network computer-vision
asked 7 mins ago
Pablo MessinaPablo Messina
435
$endgroup$
add a comment |
$begingroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to develop the visual skills to detect objects and make sense of the visual world around them. My educated guess is that brains must be performing some kind of self-supervision to train their visual neural networks. Does anybody know which learning paradigm are brains using to achieve such an outstanding level of visual competence? Are there any works on replicating this paradigm in computer vision?
machine-learning neural-network computer-vision
asked 7 mins ago
Pablo MessinaPablo Messina
435
$endgroup$
In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to develop the visual skills to detect objects and make sense of the visual world around them. My educated guess is that brains must be performing some kind of self-supervision to train their visual neural networks. Does anybody know which learning paradigm are brains using to achieve such an outstanding level of visual competence? Are there any works on replicating this paradigm in computer vision?
machine-learning neural-network computer-vision
machine-learning neural-network computer-vision
asked 7 mins ago
Pablo MessinaPablo Messina
435
asked 7 mins ago
Pablo MessinaPablo Messina
435
edited 32 secs ago
Pablo Messina
asked 7 mins ago
Pablo MessinaPablo Messina
435
asked 7 mins ago
Pablo MessinaPablo Messina
435
asked 7 mins ago
Pablo MessinaPablo Messina
435
435
add a comment |
add a comment |
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