Image Classification


Here we include the list of available datasets as well as important publications on image classification via artificial neural networks.


Publications


Architecture:FixEfficientNet
Year:2020
Paper:Touvron, Hugo, et al. “Fixing the train-test resolution discrepancy: FixEfficientNet.” arXiv preprint arXiv:2003.08237 (2020).
Architecture:NoisyStudent(EfficientNet)
Year:2020
Paper:Xie, Qizhe, et al. “Self-training with noisy student improves imagenet classification.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
Architecture:BitL
Year:2019
Paper:Kolesnikov, Alexander, et al. “Big transfer (BiT): General visual representation learning.” arXiv preprint arXiv:1912.11370 (2019).
Architecture:EfficientNet
Year:2019
Paper:Tan, Mingxing, and Quoc V. Le. “Efficientnet: Rethinking model scaling for convolutional neural networks.” arXiv preprint arXiv:1905.11946 (2019).
Architecture:ResNext
Year:2018
Paper:Mahajan, Dhruv, et al. “Exploring the limits of weakly supervised pretraining.” Proceedings of the European Conference on Computer Vision (ECCV). 2018.
Architecture:SENet
Year:2018
Paper:Hu, Jie, Li Shen, and Gang Sun. “Squeeze-and-excitation networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
Architecture:ResNext
Year:2017
Paper:Xie, Saining, et al. “Aggregated residual transformations for deep neural networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
Architecture:Inception-ResNet
Year:2017
Paper:Szegedy, Christian, et al. “Inception-v4, inception-resnet and the impact of residual connections on learning.” AAAI. Vol. 4. 2017.
Architecture:DenseNet
Year:2017
Paper:Huang, Gao, et al. “Densely connected convolutional networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
Architecture:WideResNet
Year:2016
Paper:Zagoruyko, Sergey, and Nikos Komodakis. “Wide residual networks.” arXiv preprint arXiv:1605.07146 (2016).
Architecture:ResNet
Year:2016
Paper:He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Architecture:GoogLeNet
Year:2014
Paper:Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
Architecture:VGG
Year:2014
Paper:Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
Architecture:AlexNet
Year:2012
Paper:Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
Architecture:LeNet5
Year:1998
Paper:LeCun, Yann, et al. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324.