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Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
A residual block in a deep residual network. Here, the residual connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs.
Inception is a 2010 science fiction action film written and directed by Christopher Nolan, who also produced it with Emma Thomas, his wife, under their label Syncopy.It stars Leonardo DiCaprio as a professional thief who steals information by infiltrating the dreams of his victims and is promised to have his criminal status cleared as payment for the implantation of an idea into a target's ...
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model.
Vision Transformer architecture, showing the encoder-only Transformer blocks inside. The basic architecture, used by the original 2020 paper, [1] is as follows. In summary, it is a BERT-like encoder-only Transformer.
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
AlphaGo Zero is a version of DeepMind's Go software AlphaGo.AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. [1]
AlexNet contains eight layers: the first five are convolutional layers, some of them followed by max-pooling layers, and the last three are fully connected layers. The network, except the last layer, is split into two copies, each run on one GPU. [1]