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Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. [1] Application domains include cluster analysis in computer vision and image processing .
Catastrophic Remembering may often occur as an outcome of elimination of catastrophic interference by using a large representative training set or enough sequential memory sets (memory replay or data rehearsal), leading to a breakdown in discrimination between input patterns that have been learned and those that have not. [33]
Memory impairment affects both novel and familiar experiences. Poor memory after damage to the brain is usually considered to result from information being lost or rendered inaccessible. [22] With such impairment it is assumed that it must be due to the incorrect interpretation of previously encountered information as being novel. [22]
The "loss layer", or "loss function", specifies how training penalizes the deviation between the predicted output of the network, and the true data labels (during supervised learning). Various loss functions can be used, depending on the specific task. The Softmax loss function is used for predicting a single class of K mutually exclusive classes.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
Computer vision is an interdisciplinary field related to, e.g., artificial intelligence, machine learning, robotics, signal processing and geometry. The purpose of computer vision is to program a computer to "understand" a scene or features in an image. Computer vision shares many topics and methods with image processing and machine vision ...
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A convolutional neural network layer, in the context of computer vision, can be considered a GNN applied to graphs whose nodes are pixels and only adjacent pixels are connected by edges in the graph. A transformer layer, in natural language processing , can be considered a GNN applied to complete graphs whose nodes are words or tokens in a ...