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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. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
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
It is not a model. [22] The original T5 codebase was implemented in TensorFlow with MeshTF. [2] UL2 20B (2022): a model with the same architecture as the T5 series, but scaled up to 20B, and trained with "mixture of denoisers" objective on the C4. [23] It was trained on a TPU cluster by accident, when a training run was left running ...
DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images.
Before LeNet-1, the 1988 architecture [3] was a hybrid approach. The first stage scaled, deskewed, and skeletonized the input image. The second stage was a convolutional layer with 18 hand-designed kernels. The third stage was a fully connected network with one hidden layer. The LeNet-1 architecture has 3 hidden layers (H1-H3) and an output ...
This page was last edited on 26 November 2021, at 16:57 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
Specifically, consider a language model that given a previous text , predicts the next word . The network encodes the text into a vector v c {\displaystyle v_{c}} , and predicts the probability distribution of the next word as S o f t m a x ( v c W ) {\displaystyle \mathrm {Softmax} (v_{c}W)} for an embedding matrix W {\displaystyle W} .
Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5] The U-Net architecture has also been employed in diffusion models for iterative image denoising. [6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.