Book ChapterDOI
GradientBased Learning Applied to Document Recognition
Simon Haykin,Bart Kosko +1 more
- pp 306-351
TLDR
Various methods applied to handwritten character recognition are reviewed and compared and Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.Abstract:
Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks. A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.read more
Citations
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Multi-manifold deep metric learning for image set classification
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Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
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TL;DR: This paper introduces an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model by a simple model-agnostic procedure, and finds directions corresponding to sensible semantic manipulations without any form of (self-)supervision.
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Cross-Domain Self-Supervised Multi-task Feature Learning Using Synthetic Imagery
Zhongzheng Ren,Yong Jae Lee +1 more
TL;DR: A novel multi-task deep network to learn generalizable high-level visual representations based on adversarial learning is proposed and it is demonstrated that the network learns more transferable representations compared to single-task baselines.
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FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.
TL;DR: This work proposes a novel joint upsampling module named Joint Pyramid Upsampling (JPU), which achieves the state-of-the-art performance in Pascal Context dataset and ADE20K dataset while running 3 times faster.
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Improved musical onset detection with Convolutional Neural Networks
Jan Schlüter,Sebastian Böck +1 more
TL;DR: It is shown that CNNs outperform the previous state-of-the-art while requiring less manual preprocessing, suggesting that even for well-understood signal processing tasks, machine learning can be superior to knowledge engineering.