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

Gradient-based learning applied to document recognition

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

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Going Deeper with Convolutions

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Skeleton-based action recognition via spatial and temporal transformer networks

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A cloud detection algorithm for satellite imagery based on deep learning

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Large-Scale Study of Curiosity-Driven Learning

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Pathology image analysis using segmentation deep learning algorithms

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Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100

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A Generalization of Transformer Networks to Graphs

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Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

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A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning

Mundhenk, T. N., Konjevod, G., Sakla, W. A., & Boakye, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_48 · Cited by 386

Hierarchical Multiscale Recurrent Neural Networks

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Deep learning with convolutional neural network in radiology

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A review on modern defect detection models using DCNNs – Deep convolutional neural networks

Tulbure, A.-A., Tulbure, A.-A., & Dulf, E.-H. (2022) · Journal of Advanced Research · doi:10.1016/j.jare.2021.03.015 · Cited by 372

Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network

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SqueezeNext: Hardware-Aware Neural Network Design

Gholami, A., Kwon, K., Wu, B., Tai, Z., Yue, X., Jin, P., Zhao, S., & Keutzer, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00215 · Cited by 350

Combining Residual Networks with LSTMs for Lipreading

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No New-Net

Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11726-9_21 · Cited by 341

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334

A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data

Challa, S. K., Kumar, A., & Semwal, V. B. (2022) · The Visual Computer · doi:10.1007/s00371-021-02283-3 · Cited by 338

A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data

Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338

Local Relation Learning for Face Forgery Detection

Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16193 · Cited by 334

MoDNN: Local distributed mobile computing system for Deep Neural Network

Mao, J., Chen, X., Nixon, K. W., Krieger, C., & Chen, Y. (2017) · Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 · doi:10.23919/date.2017.7927211 · Cited by 331

Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

Ye, J., Lu, X., Lin, Z., & Wang, J. Z. (2018) · arXiv · doi:10.48550/arxiv.1802.00124 · Cited by 328

Parallel Feature Pyramid Network for Object Detection

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Learning to optimize: Training deep neural networks for wireless resource management

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A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI

Tran, P. V. (2016) · arXiv · doi:10.48550/arxiv.1604.00494 · Cited by 323

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Ioannou, Y., Robertson, D., Cipolla, R., & Criminisi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.633 · Cited by 321

Dynamic Multi-Scale Filters for Semantic Segmentation

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DeepMedic for Brain Tumor Segmentation

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Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging

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Imaging and fusing time series for wearable sensor-based human activity recognition

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Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

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Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke

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Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness

Li, G., Yang, Y., Li, S., Qu, X., Lyu, N., & Li, S. E. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2021.103452 · Cited by 308

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., & Ouni, K. (2019) · 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS) · doi:10.1109/uvs.2019.8658300 · Cited by 307

On the importance of single directions for generalization

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Convolutional neural networks for multi-class brain disease detection using MRI images

Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101673 · Cited by 301

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

Zhu, B., Jiang, Z., Zhou, X., Li, Z., & Yu, G. (2019) · arXiv · doi:10.48550/arxiv.1908.09492 · Cited by 297

Local Binary Convolutional Neural Networks

Juefei-Xu, F., Boddeti, V. N., & Savvides, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.456 · Cited by 297

Birnet: Brain image registration using dual-supervised fully convolutional networks

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SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?

McCormac, J., Handa, A., Leutenegger, S., & Davison, A. J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.292 · Cited by 291

Towards Reverse-Engineering Black-Box Neural Networks

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Expert-level sleep scoring with deep neural networks

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Advancing High Fidelity Identity Swapping for Forgery Detection

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Toward robust mammography-based models for breast cancer risk

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Semisupervised hyperspectral image classification based on Generative Adversarial Networks

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Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos, A., Azkune, G., & Arganda-Carreras, I. (2017) · Wireless Communications and Mobile Computing · doi:10.1155/2017/9474806 · Cited by 282

Neural Photo Editing with Introspective Adversarial Networks

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Scaling Egocentric Vision: The EPIC-KITCHENS Dataset

Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.02748 · Cited by 274

Deep convolutional neural networks for mammography: advances, challenges and applications

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MONet: Unsupervised Scene Decomposition and Representation

Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., & Lerchner, A. (2019) · arXiv · doi:10.48550/arxiv.1901.11390 · Cited by 270

An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification

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A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Mehmood, A., Maqsood, M., Bashir, M., & Shuyuan, Y. (2020) · Brain Sciences · doi:10.3390/brainsci10020084 · Cited by 264

Deep multimodal fusion for semantic image segmentation: A survey

Zhang, Y., Sidibé, D., Morel, O., & Mériaudeau, F. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104042 · Cited by 261

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

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Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease

Madani, A., Ong, J. R., Tibrewal, A., & Mofrad, M. R. K. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0065-x · Cited by 250

Collaborative Video Object Segmentation by Foreground-Background Integration

Yang, Z., Wei, Y., & Yang, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58558-7_20 · Cited by 257

Development of convolutional neural network and its application in image classification: a survey

Wang, W., Yang, Y., Wang, X., Wang, W., & Li, J. (2019) · Optical Engineering · doi:10.1117/1.oe.58.4.040901 · Cited by 252

RetinaTrack: Online Single Stage Joint Detection and Tracking

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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., & Wilson, A. G. (2018) · arXiv · doi:10.48550/arxiv.1802.10026 · Cited by 252

A federated learning system with enhanced feature extraction for human activity recognition

Xiao, Z., Xu, X., Xing, H., Song, F., Wang, X., & Zhao, B. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107338 · Cited by 251

Robust Multi-Modality Multi-Object Tracking

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A Novel PAPR Reduction Scheme for OFDM System Based on Deep Learning

Kim, M., Lee, W., & Cho, D.-H. (2018) · IEEE Communications Letters · doi:10.1109/lcomm.2017.2787646 · Cited by 241

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Ashmore, R., Calinescu, R., & Paterson, C. (2022) · ACM Computing Surveys · doi:10.1145/3453444 · Cited by 256

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Wu, H., Judd, P., Zhang, X., Isaev, M., & Micikevicius, P. (2020) · arXiv · doi:10.48550/arxiv.2004.09602 · Cited by 246

CrypTFlow: Secure TensorFlow Inference

Kumar, N., Rathee, M., Chandran, N., Gupta, D., Rastogi, A., & Sharma, R. (2020) · 2020 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40000.2020.00092 · Cited by 245

MARTA GANs: unsupervised representation learning for remote sensing image classification

Lin, D., Fu, K., Wang, Y., Xu, G., & Sun, X. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2752750 · Cited by 230

How to Train a Compact Binary Neural Network with High Accuracy?

Tang, W., Hua, G., & Wang, L. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10862 · Cited by 241

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., & Gratacós, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-67076-5 · Cited by 240

Towards reconstructing intelligible speech from the human auditory cortex

Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D., & Mesgarani, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-37359-z · Cited by 221

FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

Li, L., Bao, J., Yang, H., Chen, D., & Wen, F. (2019) · arXiv · doi:10.48550/arxiv.1912.13457 · Cited by 236

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852172 · Cited by 240

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

Cheng, J., Nguyen, T. Y. D., Cygan, K. J., Çelik, M. H., Fairbrother, W. G., Avsec, Ž., & Gagneur, J. (2019) · Genome Biology · doi:10.1186/s13059-019-1653-z · Cited by 235

Emotion Recognition using Multimodal Residual LSTM Network

Ma, J., Tang, H., Zheng, W.-L., & Lu, B.-L. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3350871 · Cited by 230

Hypernetwork Knowledge Graph Embeddings

Balažević, I., Allen, C., & Hospedales, T. M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-30493-5_52 · Cited by 229

WRPN: Wide Reduced-Precision Networks

Mishra, A., Nurvitadhi, E., Cook, J. J., & Marr, D. (2017) · arXiv · doi:10.48550/arxiv.1709.01134 · Cited by 227

Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research.

Lipton, Z. C., & Steinhardt, J. (2019) · Queue · doi:10.1145/3317287.3328534 · Cited by 231

3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas

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TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT

Lin, K., Xu, X., & Gao, H. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107974 · Cited by 226

Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

Nutkiewicz, A., Yang, Z., & Jain, R. K. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.023 · Cited by 223

End-to-End Object Detection with Fully Convolutional Network

Wang, J., Song, L., Li, Z., Sun, H., Sun, J., & Zheng, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01559 · Cited by 235

Fully Quantized Network for Object Detection

Li, R., Wang, Y., Liang, F., Qin, H., Yan, J., & Fan, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00292 · Cited by 214

Deep Residual Neural Networks for Audio Spoofing Detection

Alzantot, M., Wang, Z., & Srivastava, M. B. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-3174 · Cited by 215

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine

Özyurt, F., Sert, E., & Avcı, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109433 · Cited by 210

Real-time apple detection system using embedded systems with hardware accelerators: an edge ai application

Mazzia, V., Khaliq, A., Salvetti, F., & Chiaberge, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964608 · Cited by 212

Orthographic feature transform for monocular 3d object detection

Roddick, T., Kendall, A., & Cipolla, R. (2018) · arXiv · doi:10.48550/arxiv.1811.08188 · Cited by 211

Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure

Amini, A., Soleimany, A. P., Schwarting, W., Bhatia, S. N., & Rus, D. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314243 · Cited by 206

Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

Jiang, T., Li, Y., Xie, W., & Du, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2965961 · Cited by 209

Structured prediction models for RNN based sequence labeling in clinical text

Jagannatha, A., & yu, H. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1082 · Cited by 215

ExprGAN: Facial Expression Editing With Controllable Expression Intensity

Ding, H., Sricharan, K., & Chellappa, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12277 · Cited by 207

Cross-view image synthesis using conditional GANs

Regmi, K., & Borji, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00369 · Cited by 204

Deep convolutional neural network for segmentation of knee joint anatomy

Zhou, Z., Zhao, G., Kijowski, R., & Liu, F. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27229 · Cited by 202

Country-wide high-resolution vegetation height mapping with Sentinel-2

Lang, N., Schindler, K., & Wegner, J. D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111347 · Cited by 202

A survey of swarm and evolutionary computing approaches for deep learning

Darwish, A., Hassanien, A. E., & Das, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09719-2 · Cited by 200

Dual-force convolutional neural networks for accurate brain tumor segmentation

Chen, S., Ding, C., & Liu, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.009 · Cited by 195

Quantitative susceptibility mapping using deep neural network: QSMnet

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Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

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Scalable Methods for 8-bit Training of Neural Networks

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Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks

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Privacy Risks of General-Purpose Language Models

Pan, X., Zhang, M., Ji, S., & Yang, M. (2020) · 2020 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40000.2020.00095 · Cited by 188

Multimodal Deep Learning for Cervical Dysplasia Diagnosis

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Learning to generate images with perceptual similarity metrics

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