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Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence

Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence. Radoslaw M. Cichy. Construction of the object deep neural model ( object DNN. Figure: Object DNN architecture. The object DNN was trained to perform object categorization on everyday object categories (683 categories, with 1300 images in each category from the ImageNet database [2]) using back propagation. You can download it here. Voting on each of the 118 images ...

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Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence | brainmodels.csail.mit.edu Reviews
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Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence. Radoslaw M. Cichy. Construction of the object deep neural model ( object DNN. Figure: Object DNN architecture. The object DNN was trained to perform object categorization on everyday object categories (683 categories, with 1300 images in each category from the ImageNet database [2]) using back propagation. You can download it here. Voting on each of the 118 images ...
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1 aditya khosla
2 dimitrios pantazis
3 antonio torralba
4 aude oliva
5 acknowledgements
6 references
7 bibtex
8 coupons
9 reviews
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aditya khosla,dimitrios pantazis,antonio torralba,aude oliva,acknowledgements,references,bibtex
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Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence | brainmodels.csail.mit.edu Reviews

https://brainmodels.csail.mit.edu

Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence. Radoslaw M. Cichy. Construction of the object deep neural model ( object DNN. Figure: Object DNN architecture. The object DNN was trained to perform object categorization on everyday object categories (683 categories, with 1300 images in each category from the ImageNet database [2]) using back propagation. You can download it here. Voting on each of the 118 images ...

INTERNAL PAGES

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1

MEG2 classification

http://brainmodels.csail.mit.edu/dnn/predictions

Prediction of Object DNN on 118 image-set. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categories (green - correct):. Predicted categ...

2

Visualizing Neuron Receptive Fields

http://brainmodels.csail.mit.edu/dnn/rf

Click the following to see the visualization of the receptive fields of different layers of the Object DNN:.

3

DrawNet

http://brainmodels.csail.mit.edu/dnn/drawCNN

Each unit corresponds to one convolutional unit. Click on one unit to show the strongest connections going in and out of the unit. For selected units, each pannel shows the 4 images (from ImageNet and Places) that most strongly activate each unit. DrawNet was originally created by Antonio Torralba.

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Computational Perception & Cognition

http://cvcl.mit.edu/publications.html

Principal Investigator: Aude Oliva. Zhou, B., Khosla, A., Lapedriza , A., Oliva, A., and Torralba, A. (coming soon). Places: An Image Database for Deep Scene Understanding. Bylinskii, Z., Recasens, A., Borji , A., Oliva, A., Torralba, A. and Durand, F. (in press, 2016). Where should saliency models look next? Proceedings of the European Conference in Computer Vision (ECCV), Amsterdam. Proceedings of Computer Vision and Pattern Recogntion, CVPR 2016. Bainbridge, W.A., Dilks, D., and Oliva, A&#...Khaligh-R...

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Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence

Comparing deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals a hierarchical correspondence. Radoslaw M. Cichy. Construction of the object deep neural model ( object DNN. Figure: Object DNN architecture. The object DNN was trained to perform object categorization on everyday object categories (683 categories, with 1300 images in each category from the ImageNet database [2]) using back propagation. You can download it here. Voting on each of the 118 images ...

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