Skip to main content

From Cats to Convolutional Neural Networks

Widely used in image recognition, Convolutional Neural Networks (CNNs) consist of multiple layers of neuron collection which look at small window of the input image, called receptive fields.

The history of Convolutional Neural Networks begins with a famous experiment “Receptive Fields of Single Neurons in the Cat’s Striate Cortex” conducted by Hubel and Wiesel. The experiment confirmed the long belief of neurobiologists and psychologists that the neurons in the brain act as feature detectors.

The first neural network model that drew inspiration from the hierarchy model of the visual nervous system proposed by Hubel and Wiesel was Neocognitron invented by Kunihiko Fukushima, and had the ability of performing unsupervised learning. Kunihiko Fukushima’s approach was commendable as it was the first neural network model having the capability of pattern recognition similar to human brain. The model gave a lot of insight and helped future understanding of the brain.

A successful advancement in Neocognitron was achieved by Yann LeCun and his team for isolated character recognition. The Convolutional Neural Network architecture was called LeNet-5 and it turned out much efficient than other classifiers. Today, in image classification, CNNs are preferred neural networks due to their in built feature learning mechanism.

CNNs have achieved the lowest error rate of 0.23 on MNIST database, a 34% improvement compared with networks [4]. CNNs are widely used in facial and object recognition.

CNNs have come a long way since the cat experiment, and are contributing a lot to make machines intelligent. Today CNNs are used in numerous applications including document recognition, object recognition, video surveillance, face detection.

References:

  1. Hubel, D. and Wiesel, T. (1959): Receptive fields of Single Neurones In The Cat’s Striate Cortex. Journal of Physiology, 195, 574-591.
  2. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202.
  3. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. (November, 1998): Gradient-based learning applied to document recognition. Proceedings of the IEEE.
  4. Ciresan, D.; Meier, U.; Schmidhuber, J., "Multi-column deep neural networks for image classification," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on , vol., no., pp.3642-3649, 16-21 June 2012
Labels:

Comments

  1. The data newsletter20 January 2022 at 10:36

    I generally check this kind of article and I found your article which is related to my interest.sequence to sequence learning with neural networks Genuinely it is good and instructive information. Thankful to you for sharing an article like this.

    ReplyDelete

Post a Comment

Popular posts from this blog

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe applicati
8 comments
Read more

Building Commonsense in AI

It is often debated that what makes humans the ultimate intelligent species is the innate quality of doing commonsense reasoning. Humans use common sense knowledge about the world around to take appropriate decisions, and this turns out to be the necessary ingredient for their survival. AI researches have long thought about building commonsense knowledge in AI. They argue that if AI possess necessary commonsense knowledge then it will be a truly intelligent machine. We will discuss two major commonsense projects that exploit this idea: Cyc tries to build a comprehensive ontology and knowledge base of everyday commonsense knowledge. This knowledge can be used by AI applications to do human-like reasoning. Started in 1984, Cyc has come a long way. Today, OpenCyc 4.0 includes the entire Cyc ontology, containing 239,000 concepts and 2,093,000 facts and can be browsed on the OpenCyc website - http://www.cyc.com/platform/opencyc/ . OpenCyc is available for download from Source
1 comment
Read more

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. ( http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf )  What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting.  A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. T
Post a Comment
Read more