The Image Receptive Fields Neural Network has been developed recently by our team to work directly with images without prior feature extraction. The IRF-NN is a simple feedforward multi-layer...
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IMAGE RECEPTIVE FIELDS NEURAL NETWORK - Home | irf-nn.net Reviews
https://irf-nn.net
The Image Receptive Fields Neural Network has been developed recently by our team to work directly with images without prior feature extraction. The IRF-NN is a simple feedforward multi-layer...
irf-nn.net
Presentation - IMAGE RECEPTIVE FIELDS NEURAL NETWORK
http://www.irf-nn.net/presentation.html
Image Receptive Fields Neural Network. IMAGE RECEPTIVE FIELDS NEURAL NETWORK. IRF-NN ARCHITECTURE - The raw images are directly presented to the network in vector form. The weights of the hidden layer G are organized as random Gaussian receptive fields that are randomly parameterized at initialization but not modified during learning. Only the output weights W are adapted. The IRF-NN approach allows to process images with an efficiency and simplicity similar to the one that made the success of the Reservoi...
IMAGE RECEPTIVE FIELDS NEURAL NETWORK - Home
http://www.irf-nn.net/index.html
Image Receptive Fields Neural Network. IMAGE RECEPTIVE FIELDS NEURAL NETWORK. The Image Receptive Fields Neural Network has been developed. Recently by our team to work directly with images without prior feature extraction. The IRF-NN is a simple. Feedforward multi-layer perceptron adapted to supervised image recognition. Its properties are very interesting and its results remarkable: it is possible to recognize views of 1,000 and more objects with this simple structure. Source code to try out IRF-NN.
Publications - IMAGE RECEPTIVE FIELDS NEURAL NETWORK
http://www.irf-nn.net/publications.html
Image Receptive Fields Neural Network. IMAGE RECEPTIVE FIELDS NEURAL NETWORK. Jean-Luc Buessler, Philippe Smagghe, Jean-Philippe Urban, Image receptive fields for artificial neural networks. Neurocomputing, Volume 144, 20 November 2014, Pages 258-270, ISSN 0925-2312, ( DOI:10.1016/j.neucom.2014.04.045. Paméla Daum, Jean-Luc Buessler, and Jean-Philippe Urban. 2011. Image receptive fields neural networks for object recognition. Neural Networks (IJCNN), The 2013 International Joint Conference on,.
Results - IMAGE RECEPTIVE FIELDS NEURAL NETWORK
http://www.irf-nn.net/results.html
Image Receptive Fields Neural Network. IMAGE RECEPTIVE FIELDS NEURAL NETWORK. Recognition of objects (classes). The efficiency of this technique is illustrated with several benchmarks, photo and video datasets. A few slides from our IJCNN 2013 presentation.
TRY OUT - IMAGE RECEPTIVE FIELDS NEURAL NETWORK
http://www.irf-nn.net/try-out.html
Image Receptive Fields Neural Network. IMAGE RECEPTIVE FIELDS NEURAL NETWORK. Code to try out IRF-NN. The Matlab code provided here allows you to experiment with IRF-NN for yourself. It comes as a companion and illustration to our 2014 Neural Computation paper:. Jean-Luc Buessler, Philippe Smagghe, Jean-Philippe Urban, Image receptive fields for artificial neural networks. Neurocomputing, Volume 144, 20 November 2014, Pages 258-270, ISSN 0925-2312, ( DOI:10.1016/j.neucom.2014.04.045.
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IMAGE RECEPTIVE FIELDS NEURAL NETWORK - Home
Image Receptive Fields Neural Network. IMAGE RECEPTIVE FIELDS NEURAL NETWORK. The Image Receptive Fields Neural Network has been developed. Recently by our team to work directly with images without prior feature extraction. The IRF-NN is a simple. Feedforward multi-layer perceptron adapted to supervised image recognition. Its properties are very interesting and its results remarkable: it is possible to recognize views of 1,000 and more objects with this simple structure. Source code to try out IRF-NN.
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