ConvNeXt
ConvNeXt is a family of pure convolutional neural network (CNN) models that match or beat Vision Transformers on standard vision benchmarks
Neural Networks
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Convolutional Neural Network
A convolutional neural network (CNN, or ConvNet) is a neural network that uses convolution-like linear operators in at least some layers.
Convolutional Operation
The convolutional operation is a mathematical procedure that combines two functions to produce a third function expressing how the shape of one is modified by the other.
DenseNet
DenseNet (Densely Connected Convolutional Networks) is a convolutional neural network architecture that connects every layer to every other layer in a feed-forward fashion
Depthwise separable convolutional neural network (sepCNN)
A depthwise separable convolutional neural network (often abbreviated sepCNN) is a convolutional neural network that replaces standard convolution layers with depthwise separable convolutions.
EfficientNet
EfficientNet is a family of convolutional neural network architectures and a model-scaling method that uniformly scales network depth, width, and input resolution with a single compound coefficient, developed…
Inception (deep learning)
Inception is a family of convolutional neural network (CNN) architectures developed by researchers at Google, first introduced in 2014.
LeNet
LeNet is the pioneering family of convolutional neural networks developed by Yann LeCun and collaborators at AT&T Bell Labs between roughly 1988 and 1998 to read handwritten characters
MobileNet
MobileNet is a family of efficient convolutional neural network (CNN) architectures developed by Google for mobile and edge AI applications.
NeRF
Neural Radiance Fields (NeRF) is a method for synthesizing photorealistic novel views of a 3D scene by encoding the scene as a continuous 5D function (3D position plus 2D viewing direction) inside a single…
Pooling
Pooling is a downsampling operation in neural networks that aggregates each local region of a feature map into a single summary value
ResNet
ResNet, short for residual network, is a family of deep convolutional neural networks introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016.
Spatial Pooling
Spatial pooling is a downsampling operation in convolutional neural networks (CNNs) that replaces a local region of a feature map with a single summary statistic, such as the maximum or the average of the…
Swin Transformer
The Swin Transformer (Shifted Window Transformer) is a hierarchical vision transformer architecture that computes self-attention within local, non-overlapping windows and introduces a shifted window…
Translational invariance
Translational invariance (also called translation invariance or shift invariance) is the property of a function, system, or machine learning model whose output does not change when its input is translated…
VGG
VGG (also called VGGNet) is a deep convolutional neural network architecture, introduced in 2014 by Karen Simonyan and Andrew Zisserman of the Visual Geometry Group at the University of Oxford
YOLO (object detection)
YOLO (You Only Look Once) is a family of object detection models that treat detection as a single regression problem, predicting bounding boxes and class probabilities directly from full images in one forward…