mlsanityData to 7 Oct 2026

ImageNet pre-trained models with batch normalization

arXiv 1612.01452 ↗First posted 5 Dec 2016cs.CVcited by 14

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Abstract (arXiv)

Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with popular state-of-the-art architectures for the Caffe framework. The first release includes Residual Networks (ResNets) with generation script as well as the batch-normalization-variants of AlexNet and VGG19. All models outperform previous models with the same architecture. The models and training code are available at http://www.inf-cv.uni-jena.de/Research/CNN+Models.html and https://github.com/cvjena/cnn-models

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Dec 2016.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 2; 2024: 1; 2025: 1.

14 papers.

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  1. 2022A Review of Deep Transfer Learning and Recent Advancementscited by 56
  2. 2021A Survey on Green Deep Learningcited by 52
  3. 2022A study of deep perceptual metrics for image quality assessmentcited by 5
  4. 2022Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learningcited by 5
  5. 2021Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Datacited by 1
  6. 2021Region attention and graph embedding network for occlusion objective class-based micro-expression recognitioncited by 0
  7. 2022MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networkscited by 0
  8. 2022Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Informationcited by 0
  9. 2022EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learningcited by 0
  10. 2022PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activationscited by 0
  11. 2023Revisiting Pre-training in Audio-Visual Learningcited by 0
  12. 2023AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networkscited by 0
2 more
  1. 2024Unmasking honey adulteration : a breakthrough in quality assurance through cutting-edge convolutional neural network analysis of thermal imagescited by 0
  2. 2025Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imagingcited by 0