Deep Convolutional Neural Networks (DCNNs) have achieved remarkable success in various Computer Vision applications. Like others, the task of semantic segmentation is not an exception to this trend.
Source: freecodecamp.org
We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task. This method performs as well as MAML, a broadly applicable meta-learning algorithm, while being simpler to implement and more computationally efficient.
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In 2013, DeepMind Technologies, then a little-known company, published a groundbreaking paper showing how a neural network could learn to play 1980s video games the way humans do by looking at the screen. These networks then went on to thrash the best human players.
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