Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

Going Deeper with Convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461

Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D

Philion, J., & Fidler, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58568-6_12 · Cited by 1,271

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

Mallya, A., & Lazebnik, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00810 · Cited by 1,258

Exploring the Limits of Weakly Supervised Pretraining

Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_12 · Cited by 1,224

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Foerster, J. N., Assael, Y. M., de Freitas, N., & Whiteson, S. (2016) · arXiv · doi:10.48550/arxiv.1605.06676 · Cited by 1,217

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., et al. (2018) · arXiv · doi:10.48550/arxiv.1811.06965 · Cited by 1,180

Mixed Precision Training

Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al. (2017) · arXiv · doi:10.48550/arxiv.1710.03740 · Cited by 1,179

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P.-A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2642839 · Cited by 1,140

Deep learning to represent subgrid processes in climate models

Rasp, S., Pritchard, M. S., & Gentine, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810286115 · Cited by 1,044

Deep Learning on Image Denoising: An overview

Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.025 · Cited by 1,091

MaskGAN: Towards Diverse and Interactive Facial Image Manipulation

Lee, C.-H., Liu, Z., Wu, L., & Luo, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00559 · Cited by 1,130

Recent advances in deep learning for object detection

Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research · doi:10.1101/gr.200535.115 · Cited by 1,048

Very Deep Convolutional Networks for Text Classification

Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1104 · Cited by 1,046

Federated learning on non-IID data: A survey

Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

Zhao, J., Mao, X., & Chen, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.035 · Cited by 1,044

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1703.03864 · Cited by 1,027

Data-Efficient Image Recognition with Contrastive Predictive Coding

Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv · doi:10.48550/arxiv.1905.09272 · Cited by 1,014

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., & Hajishirzi, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_34 · Cited by 1,029

Importance Estimation for Neural Network Pruning

Molchanov, P., Mallya, A., Tyree, S., Frosio, I., & Kautz, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01152 · Cited by 983

Pre-trained models: Past, present and future

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994

Stand-Alone Self-Attention in Vision Models

Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., & Shlens, J. (2019) · arXiv · doi:10.48550/arxiv.1906.05909 · Cited by 977

A survey of unsupervised deep domain adaptation

Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987

Large scale deep learning for computer aided detection of mammographic lesions

Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965

Learning Representations for Automatic Colorization

Larsson, G., Maire, M., & Shakhnarovich, G. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_35 · Cited by 946

Energy-based Generative Adversarial Network

Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912

BirdNET: A deep learning solution for avian diversity monitoring

Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101236 · Cited by 903

FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture

Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.059 · Cited by 872

Trained Ternary Quantization

Zhu, C., Han, S., Mao, H., & Dally, W. J. (2016) · arXiv · doi:10.48550/arxiv.1612.01064 · Cited by 865

Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model

Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.135 · Cited by 801

Speeding Up Distributed Machine Learning Using Codes

Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., & Ramchandran, K. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2736066 · Cited by 829

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

Kendall, A., Badrinarayanan, V., & Cipolla, R. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.57 · Cited by 784

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101694 · Cited by 809

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., & Chen, L.-C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_7 · Cited by 809

Deep Learning in Medical Ultrasound Analysis: A Review

Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798

Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

Misra, I., Zitnick, C. L., & Hebert, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_32 · Cited by 779

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., et al. (2018) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-018-0471-x · Cited by 773

Adversarially Learned One-Class Classifier for Novelty Detection

Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765

Deep Reinforcement Learning: An Overview

Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762

Identifying facial phenotypes of genetic disorders using deep learning

Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739

Density estimation using Real NVP

Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067

Ternary Weight Networks

Li, F., Liu, B., Wang, X., Zhang, B., & Yan, J. (2016) · arXiv · doi:10.48550/arxiv.1605.04711 · Cited by 732

Multimodal deep learning models for early detection of Alzheimer’s disease stage

Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-74399-w · Cited by 732

Residual Networks Behave Like Ensembles of Relatively Shallow Networks

Veit, A., Wilber, M., & Belongie, S. (2016) · arXiv · doi:10.48550/arxiv.1605.06431 · Cited by 705

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

Zhu, Y., & Zabaras, N. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.04.018 · Cited by 704

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al. (2018) · arXiv · doi:10.48550/arxiv.1806.10293 · Cited by 697

Attention-guided CNN for image denoising

Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., & Liu, H. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.024 · Cited by 688

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691

Out of Time: Automated Lip Sync in the Wild

Chung, J. S., & Zisserman, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54427-4_19 · Cited by 689

Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.

Yuan, Y., Chao, M., & Lo, Y.-C. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2695227 · Cited by 687

Fixed Point Quantization of Deep Convolutional Networks

Lin, D. D., Talathi, S. S., & Annapureddy, V. S. (2015) · arXiv · doi:10.48550/arxiv.1511.06393 · Cited by 684

DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667

UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory

Kokkinos, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.579 · Cited by 657

An automated system for epilepsy detection using EEG brain signals based on deep learning approach

Ullah, I., Hussain, M., Qazi, E.-U.-H., & Aboalsamh, H. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.021 · Cited by 622

A graph placement methodology for fast chip design

Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nova, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03544-w · Cited by 618

Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning

Zeng, A., Song, S., Welker, S., Lee, J., Rodriguez, A., & Funkhouser, T. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593986 · Cited by 606

Modeling the language of life – Deep Learning Protein Sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606

Modeling aspects of the language of life through transfer-learning protein sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3220-8 · Cited by 606

Adaptive batch normalization for practical domain adaptation

Li, Y., Wang, N., Shi, J., Hou, X., & Liu, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.005 · Cited by 601

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579

YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

Huang, R., Pedoeem, J., & Chen, C. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8621865 · Cited by 622

Self-supervised learning for medical image analysis using image context restoration

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575

Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning

Sirinam, P., Imani, M., Juarez, M., & Wright, M. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243768 · Cited by 592

Seven-point checklist and skin lesion classification using multitask multimodal neural nets

Kawahara, J., Daneshvar, S., Argenziano, G., & Hamarneh, G. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2824327 · Cited by 584

Learning to decode linear codes using deep learning

Nachmani, E., Be'ery, Y., & Burshtein, D. (2016) · 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) · doi:10.1109/allerton.2016.7852251 · Cited by 561

Evaluating deep learning architectures for Speech Emotion Recognition

Fayek, H. M., Lech, M., & Cavedon, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.02.013 · Cited by 554

T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos

Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., Zhang, C., Wang, Z., Wang, R., Wang, X., et al. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2736553 · Cited by 554

ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network

Mehta, S., Rastegari, M., Shapiro, L., & Hajishirzi, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00941 · Cited by 551

Deep visual tracking: Review and experimental comparison

Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517

Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion

Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K., & Kautz, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00874 · Cited by 540

Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal

Guan, S., Khan, A. A., Sikdar, S., & Chitnis, P. V. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2912935 · Cited by 538

Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

Shen, S., Sadoughi, M., Li, M., Wang, Z., & Hu, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114296 · Cited by 537

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2016) · arXiv · doi:10.48550/arxiv.1610.02357 · Cited by 538

Lip Reading in the Wild

Chung, J. S., & Zisserman, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54184-6_6 · Cited by 537

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection

Haliassos, A., Vougioukas, K., Petridis, S., & Pantic, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00500 · Cited by 537

Sharpness-Aware Minimization for Efficiently Improving Generalization

Foret, P., Kleiner, A., Mobahi, H., & Neyshabur, B. (2020) · arXiv · doi:10.48550/arxiv.2010.01412 · Cited by 521

Deep convolutional neural networks for Raman spectrum recognition: a unified solution

Liu, J., Osadchy, M., Ashton, L., Foster, M., Solomon, C. J., & Gibson, S. J. (2017) · The Analyst · doi:10.1039/c7an01371j · Cited by 507

A scoping review of transfer learning research on medical image analysis using ImageNet

Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508

TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals

Gao, J., Yang, Z., Sun, C., Chen, K., & Nevatia, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.392 · Cited by 498

CrowdHuman: A Benchmark for Detecting Human in a Crowd

Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., & Sun, J. (2018) · arXiv · doi:10.48550/arxiv.1805.00123 · Cited by 534

SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

Bai, Y., Zhang, Y., Ding, M., & Ghanem, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_13 · Cited by 487

Rotation Equivariant CNNs for Digital Pathology

Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00934-2_24 · Cited by 476

Brain-inspired replay for continual learning with artificial neural networks

van de Ven, G. M., Siegelmann, H. T., & Tolias, A. S. (2020) · Nature Communications · doi:10.1038/s41467-020-17866-2 · Cited by 474

An unexpectedly large count of trees in the West African Sahara and Sahel

Brandt, M., Tucker, C. J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L. V., Hiernaux, P., Diouf, A. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2824-5 · Cited by 466

Regularizing and Optimizing LSTM Language Models

Merity, S., Keskar, N. S., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1708.02182 · Cited by 697

Untitled work

(2021) · International Conference on Learning Representations · Cited by 449

Generating Videos with Scene Dynamics

Vondrick, C., Pirsiavash, H., & Torralba, A. (2016) · Maryland Shared Open Access Repository · doi:10.13016/m26gih-tnyz · Cited by 441

A review of Convolutional-Neural-Network-based action recognition

Yao, G., Lei, T., & Zhong, J. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.05.018 · Cited by 437

LOGAN: Membership Inference Attacks Against Generative Models

Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0008 · Cited by 433

Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation

Kwon, Y., Won, J.-H., Kim, B. J., & Paik, M. C. (2020) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2019.106816 · Cited by 424

YOLO9000: Better, Faster, Stronger

Redmon, J., & Farhadi, A. (2016) · arXiv · doi:10.48550/arxiv.1612.08242 · Cited by 488

A Simple Baseline for Bayesian Uncertainty in Deep Learning

Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., & Wilson, A. G. (2019) · arXiv · doi:10.48550/arxiv.1902.02476 · Cited by 429

Multi-Task Learning with Deep Neural Networks: A Survey

Crawshaw, M. (2020) · arXiv · doi:10.48550/arxiv.2009.09796 · Cited by 428

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

van Grinsven, M. J. J. P., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sanchez, C. I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526689 · Cited by 426

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

Ding, H., Zhou, S. K., & Chellappa, R. (2017) · 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) · doi:10.1109/fg.2017.23 · Cited by 416

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2712367 · Cited by 416

Deep residual learning-based fault diagnosis method for rotating machinery

Zhang, W., Li, X., & Ding, Q. (2019) · ISA Transactions · doi:10.1016/j.isatra.2018.12.025 · Cited by 416

Learned Step Size Quantization

Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., & Modha, D. S. (2019) · arXiv · doi:10.48550/arxiv.1902.08153 · Cited by 415

Untitled work

(2016) · doi:10.1007/978-3-319-46493-0_51 · Cited by 411