It is a general, hierarchical feature extractor that maps raw pixel intensities of the input image into a feature vector to be classified by several, usually 2 ...
We use a fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors.
Our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two.
Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementation of a Deep ...
Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementation of a Deep ...
This paper presents a deep convolutional neural network architecture to classify the traffic signs of the GTSRB dataset using a filter bank to extract more ...
Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementation of a Deep ...
On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image ...
Multi-column Deep Neural Networks for Image Classification
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Oct 22, 2024 · Our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two.
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