Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Generative adversarial networkA generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.
Corner detectionCorner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, , video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point.
Learning organizationIn business management, a learning organization is a company that facilitates the learning of its members and continuously transforms itself. The concept was coined through the work and research of Peter Senge and his colleagues. Learning organizations may develop as a result of the pressures facing modern organizations; this enables them to remain competitive in the business environment. There are many definitions of a learning organization as well as typologies of kinds of learning organizations.
Organizational learningOrganizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains experience. From this experience, it is able to create knowledge. This knowledge is broad, covering any topic that could better an organization. Examples may include ways to increase production efficiency or to develop beneficial investor relations. Knowledge is created at four different units: individual, group, organizational, and inter organizational.
Pixel artPixel art is a form of digital art drawn with graphical software where images are built using pixels as the only building block. It is widely associated with the low-resolution graphics from 8-bit and 16-bit era computers and arcade video game consoles, in addition to other limited systems such as LED displays and graphing calculators, which have a limited number of pixels and colors available. The art form is still employed to this day by pixel artists and game studios, even though the technological limitations have since been surpassed.
Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
Asch conformity experimentsIn psychology, the Asch conformity experiments or the Asch paradigm were a series of studies directed by Solomon Asch studying if and how individuals yielded to or defied a majority group and the effect of such influences on beliefs and opinions. Developed in the 1950s, the methodology remains in use by many researchers. Uses include the study of conformity effects of task importance, age, sex, and culture. Many early studies in social psychology were adaptations of earlier work on "suggestibility" whereby researchers such as Edward L.
Random sample consensusRandom sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed.
Filter (signal processing)In signal processing, a filter is a device or process that removes some unwanted components or features from a signal. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal. Most often, this means removing some frequencies or frequency bands. However, filters do not exclusively act in the frequency domain; especially in the field of many other targets for filtering exist.
