Multi-column deep neural networks for image classification

Citations Over TimeTop 1% of 2012 papers

Abstract

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. 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 classification benchmarks.

Related Papers

• → Handwriting Recognition using Artificial Intelligence Neural Network and Image Processing(2020)44 cited
• → Handwriting Recognition System- A Review(2019)8 cited
• → Writer Verification of Arabic Handwriting(2008)9 cited
• → Analysis of Signature Handwriting Features and Key Points of Inspection after exercise(2020)
• → A Review of Robust Automated Handwriting Recognition System(2024)