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Paper: convolutional networks [LeCun et al., 1998a]

In their influential 1998 paper titled "Gradient-Based Learning Applied to Document Recognition," Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner introduced and detailed the architecture and application of Convolutional Neural Networks (CNNs) for document recognition tasks.  

Key Contributions:

  1. Introduction of CNN Architecture:

    • The authors presented a multi-layered neural network designed to process two-dimensional image data with minimal preprocessing.

    • The architecture incorporated:

      • Convolutional Layers: To automatically and adaptively learn spatial hierarchies of features from input images.

      • Subsampling (Pooling) Layers: To reduce the dimensionality of feature maps, thereby decreasing computational load and providing some degree of translation invariance.

  2. Application to Handwritten Digit Recognition:

    • The paper demonstrated the effectiveness of CNNs by applying them to the recognition of handwritten digits.

    • The proposed CNN, known as LeNet-5, achieved high accuracy rates, showcasing the model's capability to learn complex representations directly from pixel data.

  3. End-to-End Training with Gradient-Based Learning:

    • The network was trained using the backpropagation algorithm, allowing for end-to-end optimization of all parameters.

    • This approach enabled the system to learn feature extraction and classification simultaneously, streamlining the recognition process.

Impact:

This work laid the foundation for modern deep learning approaches in computer vision. The principles and architecture introduced have been extended and refined, leading to significant advancements in fields such as image and speech recognition.

Link: https://ieeexplore.ieee.org/abstract/document/726791

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