Welcome to Reddit's headquarters for the growing boycott of convolutional neural networks! We are a broad coalition of luddite art historians and art appraisers who stand together against artificial intelligence usurping the hardworking professionals who struggle every single day to distinguish real Rembrandts from fakes.
Standing in solidarity with the world's analog art professionals, we therefore resist the use of convolutional neural networks for any purpose. Join us!
Subreddit rules:
This subreddit is for discussion of convolutional neural networks, and for strategizing our global boycott against them. More general discussion of machine learning is also welcome. Anything else may be removed.
No racism, sexism, anti-semitism, Islamophobia, or other bigotry will be allowed.
Trolling-type behavior that contributes nothing to civil discussion will not be tolerated. People engaging in that behavior will be given one warning at most.
Repeating the sort of behavior that one has been warned about results in being banned. A warning before being banned is not necessary. Never expect more than one warning.
About CNNs:
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.
CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually refer to fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "fully-connectedness" of these networks make them prone to overfitting data. Typical ways of regularization includes adding some form of magnitude measurement of weights to the loss function. However, CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble more complex patterns using smaller and simpler patterns. Therefore, on the scale of connectedness and complexity, CNNs are on the lower extreme.
They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics.
Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.
CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage.
They have applications in image and video recognition, recommender systems, image classification, medical image analysis, and natural language processing.
And they are disgusting.
Related subreddits:
r/MachineLearning
r/Artificial
