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Perceiver
Perceiver is a transformer adapted to be able to process non-textual data, such as images, sounds and video, and spatial data. Transformers underlie other notable systems such as BERT and GPT-3, which preceded Perceiver. It adopts an asymmetric attention mechanism to distill inputs into a latent bottleneck, allowing it to learn from large amounts of heterogeneous data. Perceiver matches or outperforms specialized models on classification tasks. Perceiver was introduced in June 2021 by DeepMind. It was followed by Perceiver IO in August 2021. Design Perceiver is designed without modality-specific elements. For example, it does not have elements specialized to handle images, or text, or audio. Further it can handle multiple correlated input streams of heterogeneous types. It uses a small set of latent units that forms an attention bottleneck through which the inputs must pass. One benefit is to eliminate the quadratic scaling problem found in early transformers. Earlier work used c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Attention (machine Learning)
In artificial neural networks, attention is a technique that is meant to mimic cognitive attention. The effect enhances some parts of the input data while diminishing other parts — the motivation being that the network should devote more focus to the small, but important, parts of the data. Learning which part of the data is more important than another depends on the context, and this is trained by gradient descent. Attention-like mechanisms were introduced in the 1990s under names like multiplicative modules, sigma pi units, and hyper-networks. Its flexibility comes from its role as "soft weights" that can change during runtime, in contrast to standard weights that must remain fixed at runtime. Uses of attention include memory in neural Turing machines, reasoning tasks in differentiable neural computers, language processing in transformers, and LSTMs, and multi-sensory data processing (sound, images, video, and text) in perceivers. There are several types of attention includ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Transformer (machine Learning Model)
A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing (NLP) and computer vision (CV). Like recurrent neural networks (RNNs), transformers are designed to process sequential input data, such as natural language, with applications towards tasks such as translation and text summarization. However, unlike RNNs, transformers process the entire input all at once. The attention mechanism provides context for any position in the input sequence. For example, if the input data is a natural language sentence, the transformer does not have to process one word at a time. This allows for more parallelization than RNNs and therefore reduces training times. Transformers were introduced in 2017 by a team at Google Brain and are increasingly the model of choice for NLP problems, replacing RNN models such as long short-term memor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
StarCraft II
''StarCraft II'' is a military science fiction video game created by Blizzard Entertainment as a sequel to the successful ''StarCraft'' video game released in 1998. Set in a fictional future, the game centers on a galactic struggle for dominance among the various fictional races of StarCraft. ''StarCraft II'' single-player campaign is split into three installments, each of which focuses on one of the three races: '' StarCraft II: Wings of Liberty'' (released 2010), ''Heart of the Swarm'' (2013) and ''Legacy of the Void'' (2015). A final campaign pack called '' StarCraft II: Nova Covert Ops'' was released in 2016. ''StarCraft II'' multi-player gameplay spawned a separate e-sports competition that later drew interest from companies other than Blizzard, and attracted attention in South Korea and elsewhere, similar to the original ''StarCraft'' e-sports. Since 2017, ''StarCraft II'' multi-player mode, co-op mode and the first single-player campaign have been free-to-play. Story ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pixel
In digital imaging, a pixel (abbreviated px), pel, or picture element is the smallest addressable element in a raster image, or the smallest point in an all points addressable display device. In most digital display devices, pixels are the smallest element that can be manipulated through software. Each pixel is a sample of an original image; more samples typically provide more accurate representations of the original. The intensity of each pixel is variable. In color imaging systems, a color is typically represented by three or four component intensities such as red, green, and blue, or cyan, magenta, yellow, and black. In some contexts (such as descriptions of camera sensors), ''pixel'' refers to a single scalar element of a multi-component representation (called a ''photosite'' in the camera sensor context, although ''sensel'' is sometimes used), while in yet other contexts (like MRI) it may refer to a set of component intensities for a spatial position. Etymology The w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Neural Network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neuro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Vision Transformer
A Vision Transformer (ViT) is a transformer that is targeted at vision processing tasks such as image recognition. Vision Transformers Transformers found their initial applications in natural language processing (NLP) tasks, as demonstrated by language models such as BERT and GPT-3. By contrast the typical image processing system uses a convolutional neural network (CNN). Well-known projects include Xception, ResNet, EfficientNet, DenseNet, and Inception. Transformers measure the relationships between pairs of input tokens (words in the case of text strings), termed attention. The cost is quadratic in the number of tokens. For images, the basic unit of analysis is the pixel. However, computing relationships for every pixel pair in a typical image is prohibitive in terms of memory and computation. Instead, ViT computes relationships among pixels in various small sections of the image (e.g., 16x16 pixels), at a drastically reduced cost. The sections (with positional embeddings) a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Residual Neural Network
A residual neural network (ResNet) is an artificial neural network (ANN). It is a gateless or open-gated variant of the HighwayNet, the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. ''Skip connections'' or ''shortcuts'' are used to jump over some layers ( HighwayNets may also learn the skip weights themselves through an additional weight matrix for their gates). Typical ''ResNet'' models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between. Models with several parallel skips are referred to as ''DenseNets''. In the context of residual neural networks, a non-residual network may be described as a ''plain network''. Like in the case of Long Short-Term Memory recurrent neural networks there are two main reasons to add skip connections: to avoid the problem of vanishing gradients, thus leading to easier to optimize neural networks, where the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Optical Flow
Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and a scene. Optical flow can also be defined as the distribution of apparent velocities of movement of brightness pattern in an image. The concept of optical flow was introduced by the American psychologist James J. Gibson in the 1940s to describe the visual stimulus provided to animals moving through the world. Gibson stressed the importance of optic flow for affordance perception, the ability to discern possibilities for action within the environment. Followers of Gibson and his ecological approach to psychology have further demonstrated the role of the optical flow stimulus for the perception of movement by the observer in the world; perception of the shape, distance and movement of objects in the world; and the control of locomotion. The term optical flow is also used by roboticists, encompassing related techniq ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sintel
''Sintel'', code-named ''Project Durian'' during production, is a 2010 computer-animated fantasy short film. It was the third Blender "open movie". It was produced by Ton Roosendaal, chairman of the Blender Foundation, written by Esther Wouda, directed by Colin Levy, at the time an artist at Pixar and art direction by David Revoy, who is known for '' Pepper&Carrot'' an open source webcomic series. It was made at the Blender Institute, part of the Blender Foundation. The plot follows the character, Sintel, who is tracking down her pet Scales, a dragon. Just like the other Blender "open movies," the film was made using Blender, a free and open source software application for animation, created and supported by the Blender Foundation. The name comes from the Dutch word ''sintel'', which can mean 'cinder', or 'ember'. Overview Work began in May 2009. The film was officially released on 27 September 2010 at the Netherlands Film Festival. The online release was made available for ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Tokenization (lexical Analysis)
In computer science, lexical analysis, lexing or tokenization is the process of converting a sequence of characters (such as in a computer program or web page) into a sequence of ''lexical tokens'' (strings with an assigned and thus identified meaning). A program that performs lexical analysis may be termed a ''lexer'', ''tokenizer'', or ''scanner'', although ''scanner'' is also a term for the first stage of a lexer. A lexer is generally combined with a parser, which together analyze the syntax of programming languages, web pages, and so forth. Applications A lexer forms the first phase of a compiler frontend in modern processing. Analysis generally occurs in one pass. In older languages such as ALGOL, the initial stage was instead line reconstruction, which performed unstropping and removed whitespace and comments (and had scannerless parsers, with no separate lexer). These steps are now done as part of the lexer. Lexers and parsers are most often used for compilers, but ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
General Language Understanding Evaluation
These datasets are applied for machine learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce. Image data These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification. Facial recognition In computer vision, face images have been used extensively to develop facial recognition syst ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
