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Layer normalization. arXiv:1607.06450. 2016."},{"key":"ref22","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9992","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021 Oct 11\u201317"},{"key":"ref23","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016 Jun 27\u201330"},{"key":"ref24","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10425","article-title":"Designing network design spaces","author":"Radosavovic","year":"2020 Jun 13\u201319"},{"key":"ref25","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"1314","article-title":"Searching for MobileNetV3","author":"Howard","year":"2019 Oct 27\u2013Nov 2"},{"key":"ref26","series-title":"International Conference on Machine Learning","first-page":"10096","article-title":"EfficientNetV2: smaller models and faster training","author":"Tan","year":"2021 Jul 18\u201324"},{"key":"ref27","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11966","article-title":"A convnet for the 2020s","author":"Liu","year":"2022 Jun 18\u201324"},{"key":"ref28","series-title":"Proceedings of International Conference on Machine Learning","first-page":"6105","article-title":"Efficientnet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019 Jun 9\u201315"},{"key":"ref29","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"MobileNetV2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018 Jun 18\u201323"},{"key":"ref30","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2018 Jun 18\u201323"},{"key":"ref31","unstructured":"Simonyan K, Zisserman A. 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