An overview of a deep image reconstruction is shown. The pixel values of the input image are optimized so that the DNN features of the image are similar to those decoded from fMRI activity. A deep generator network (DGN) is optionally combined with the DNN to produce natural-looking images, in which optimization is performed at the input space of the DGN.</p
This electronic version was submitted by the student author. The certified thesis is available in th...
The clue of learning to recognize objects using neural network lies in imitation of animal neural ne...
Deep learning is a sub-field of machine learning, which inspired by the structure of human brain whe...
The mental contents of perception and imagery are thought to be encoded in hierarchical representati...
The mental contents of perception and imagery are thought to be encoded in hierarchical representati...
(A) Reconstructions with and without the DGN. The first, second, and third rows show presented image...
Deep learning is an important part of artificial intelligence, where the neural network can be an ef...
A relatively new research field of neurosciences, called Connectomics, aims to achieve a full unders...
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of ...
Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reco...
The black and gray surrounding frames indicate presented and reconstructed images respectively (reco...
Convolutional neural networks have recently achieved great success in Single Image Super-Resolution ...
This is a graphical representation of a standard feedforward DNN architecture. The DNN is fed with a...
A deep learning framework is presented that transforms the task of MR image reconstruction from rand...
Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minim...
This electronic version was submitted by the student author. The certified thesis is available in th...
The clue of learning to recognize objects using neural network lies in imitation of animal neural ne...
Deep learning is a sub-field of machine learning, which inspired by the structure of human brain whe...
The mental contents of perception and imagery are thought to be encoded in hierarchical representati...
The mental contents of perception and imagery are thought to be encoded in hierarchical representati...
(A) Reconstructions with and without the DGN. The first, second, and third rows show presented image...
Deep learning is an important part of artificial intelligence, where the neural network can be an ef...
A relatively new research field of neurosciences, called Connectomics, aims to achieve a full unders...
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of ...
Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reco...
The black and gray surrounding frames indicate presented and reconstructed images respectively (reco...
Convolutional neural networks have recently achieved great success in Single Image Super-Resolution ...
This is a graphical representation of a standard feedforward DNN architecture. The DNN is fed with a...
A deep learning framework is presented that transforms the task of MR image reconstruction from rand...
Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minim...
This electronic version was submitted by the student author. The certified thesis is available in th...
The clue of learning to recognize objects using neural network lies in imitation of animal neural ne...
Deep learning is a sub-field of machine learning, which inspired by the structure of human brain whe...