ResNet
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Explore more → **ResNet**
What is ResNet?
ResNet (Residual Network) is a deep learning architecture that introduces residual connections, also known as skip connections, to address the vanishing gradient problem and enable the training of very deep neural networks. Instead of directly learning the underlying mapping, residual blocks learn residual functions with reference to the layer inputs. ResNets are commonly used for image classification, object detection, and other computer vision tasks. They have also been applied to natural language processing and other domains.
What other technologies are related to ResNet?
ResNet Competitor Technologies
VGG
VGG is a CNN architecture that, like ResNet, can be used for image classification, object detection, and semantic segmentation. It is a competitor because it offers an alternative approach to solving similar computer vision problems.
DeiT
No summary available
DeiT (Data-efficient Image Transformers) is a vision transformer-based model that competes with ResNet in image classification tasks by using a different architectural approach based on attention mechanisms rather than convolutional layers.
EfficientNet
EfficientNet is a CNN architecture that aims to achieve better accuracy and efficiency compared to other CNNs, including ResNet. It uses a compound scaling method and competes directly with ResNet in terms of image classification performance.
AlexNet
No summary available
AlexNet is an earlier CNN architecture that was influential in the development of deep learning for computer vision. It competes with ResNet as an alternative, though generally less performant, solution for image classification.
MobileNet
MobileNet is a CNN architecture designed for mobile and embedded devices with limited computational resources. It competes with ResNet in scenarios where model size and inference speed are critical.
Inception
Inception (GoogLeNet) is another CNN architecture that offers an alternative approach to building deep networks. It can be used for similar image classification tasks, thus competing with ResNet.
Vision Transformers
Vision Transformers (ViT) represent a fundamentally different approach to image recognition by applying transformer architectures (originally designed for NLP) to images. They are direct competitors to CNN-based architectures like ResNet.
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ResNet Complementary Technologies
Open Neural Net Exchange
No summary available
Open Neural Net Exchange (ONNX) is an open format for representing machine learning models. It allows models developed in different frameworks to be interchanged, making it complementary by allowing ResNet models to be deployed across different platforms.
CNN
CNN (Convolutional Neural Networks) is the general class of neural networks that ResNet belongs to. ResNet is a specific architecture within the CNN family, making it a complementary concept.
ONNX
ONNX (Open Neural Network Exchange) is an open format for representing machine learning models. It allows models developed in different frameworks to be interchanged, making it complementary by allowing ResNet models to be deployed across different platforms.
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Which job functions commonly mention ResNet?
Applied Scientist
0.2% of all Applied Scientist jobs mention ResNet
View 27
jobs on Sumble
Machine Learning
0.2% of all Machine Learning jobs mention ResNet
View 189
jobs on Sumble
AI Engineer
0.2% of all AI Engineer jobs mention ResNet
View 144
jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention ResNet
View 179
jobs on Sumble
Engineer
159 Engineer jobs mention ResNet
View 159
jobs on Sumble
Which organizations are mentioning ResNet?
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