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Everything about SqueezeNet
Deep neural network for image classification, released 2016
SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy. Their best-performing model achieved the same accuracy as AlexNet on ImageNet classification, but has a size 510x less than it. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: SqueezeNetA 5-minute orientation
- Skim a recent paper: An improved SqueezeNet model for the diagnosis of lung cancer in CT scansInfluential work since 2022
- Read the landmark paper: COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray imagesCited 660 times
- Go sideways: Comparison of deep learning softwareA closely related topic
- Go sideways: KerasA closely related topic
📖 Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization
Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
Comparative Analysis of AlexNet, ResNet18 and SqueezeNet with Diverse Modification and Arduous Implementation
Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases
A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM
Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data
A novel wind turbine fault diagnosis method based on compressive sensing and lightweight SqueezeNet model
🆕 Influential since 2022
An improved SqueezeNet model for the diagnosis of lung cancer in CT scans
RETRACTED: SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm
A Novel Approach for Classifying Brain Tumours Combining a SqueezeNet Model with SVM and Fine-Tuning
Weight-Quantized SqueezeNet for Resource-Constrained Robot Vacuums for Indoor Obstacle Classification
Discrimination of VOCs with e-nose consisting of a single MEMS sensor using lightweight deep learning model based on SqueezeNet
📚 Books worth reading
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