Learning Multiple Layers of Features from Tiny Images

WordNet: a lexical database for English

Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015

Big/little deep neural network for ultra low power inference

Park, E., Kim, D., Kim, S., Kim, Y.-D., Kim, G., Yoon, S., & Yoo, S. (2015) · 2015 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) · doi:10.1109/codesisss.2015.7331375 · Cited by 130

Organizing memories for generalization in complementary learning systems

Sun, W., Advani, M., Spruston, N., Saxe, A., & Fitzgerald, J. E. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01382-9 · Cited by 103

Quantum Algorithms for Deep Convolutional Neural Networks

Kerenidis, I., Landman, J., & Prakash, A. (2019) · arXiv · doi:10.48550/arxiv.1911.01117 · Cited by 102

Rocket Launching: A Universal and Efficient Framework for Training Well-Performing Light Net

Zhou, G., Fan, Y., Cui, R., Bian, W., Zhu, X., & Gai, K. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11601 · Cited by 121

Improved Knowledge Distillation via Teacher Assistant

Mirzadeh, S. I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., & Ghasemzadeh, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5963 · Cited by 101

Problems and opportunities in training deep learning software systems: an analysis of variance

Pham, H. V., Qian, S., Wang, J., Lutellier, T., Rosenthal, J., Tan, L., Yu, Y., & Nagappan, N. (2020) · Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3324884.3416545 · Cited by 100

Untitled work

(2020) · neural information processing systems · Cited by 100

On Robustness and Transferability of Convolutional Neural Networks

Djolonga, J., Yung, J., Tschannen, M., Romijnders, R., Beyer, L., Kolesnikov, A., Puigcerver, J., Minderer, M., D'Amour, A., Moldovan, D., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01619 · Cited by 98

Understanding Image Representations by Measuring Their Equivariance and Equivalence

Lenc, K., & Vedaldi, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1098-y · Cited by 98

SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning

Xiao, X., Tang, Z., Li, C., Xiao, B., & Li, K. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3172310 · Cited by 99

Untitled work

(2018) · Cited by 95

Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators

Jiang, W., Lou, Q., Yan, Z., Yang, L., Hu, J., Hu, X. S., & Shi, Y. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2020.2991575 · Cited by 96

Interleaved text/image Deep Mining on a large-scale radiology database

Shin, H.-C., Lu, L., Kim, L., Seff, A., Yao, J., & Summers, R. M. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298712 · Cited by 94

Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays

Hirtzlin, T., Bocquet, M., Penkovsky, B., Klein, J.-O., Nowak, E., Vianello, E., Portal, J.-M., & Querlioz, D. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01383 · Cited by 93

Compiling KB-sized machine learning models to tiny IoT devices

Gopinath, S., Ghanathe, N., Seshadri, V., & Sharma, R. (2019) · Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3314221.3314597 · Cited by 92

NSML: Meet the MLaaS platform with a real-world case study

Kim, H., Kim, M., Seo, D., Kim, J., Park, H., Park, S., Jo, H., Kim, K., Yang, Y., Kim, Y., et al. (2018) · arXiv · doi:10.48550/arxiv.1810.09957 · Cited by 91

Incorporating neuro-inspired adaptability for continual learning in artificial intelligence

Wang, L., Zhang, X., Li, Q., Zhang, M., Su, H., Zhu, J., & Zhong, Y. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00747-w · Cited by 91

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Wang, J., Tantia, V., Ballas, N., & Rabbat, M. (2019) · arXiv · doi:10.48550/arxiv.1910.00643 · Cited by 90

μNAS: Constrained Neural Architecture Search for Microcontrollers

Liberis, E., Dudziak, Ł., & Lane, N. D. (2021) · Proceedings of the 1st Workshop on Machine Learning and Systems · doi:10.1145/3437984.3458836 · Cited by 90

Towards MLOps: A Case Study of ML Pipeline Platform

Zhou, Y., Yu, Y., & Ding, B. (2020) · 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice51518.2020.00102 · Cited by 103

Untitled work

(2018) · Neural Information Processing Systems · Cited by 89

Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability

Yang, Y., Zhou, D.-W., Zhan, D.-C., Xiong, H., & Jiang, Y. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330865 · Cited by 83

A fungus spores dataset and a convolutional neural network based approach for fungus detection

Tahir, M. W., Zaidi, N. A., Rao, A. A., Blank, R., Vellekoop, M. J., & Lang, W. (2018) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2018.2839585 · Cited by 88

A mathematical motivation for complex-valued convolutional networks

Tygert, M., Bruna, J., Chintala, S., LeCun, Y., Piantino, S., & Szlam, A. (2016) · Neural Computation · doi:10.1162/neco_a_00824 · Cited by 88

HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark

Li, C., Yu, Z., Fu, Y., Zhang, Y., Zhao, Y., You, H., Yu, Q., Wang, Y., & Lin, Y. C. (2021) · arXiv · doi:10.48550/arxiv.2103.10584 · Cited by 88

A Survey and Taxonomy of FPGA-based Deep Learning Accelerators

Blaiech, A. G., Ben Khalifa, K., Valderrama, C., Fernandes, M. A. C., & Bedoui, M. H. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.01.007 · Cited by 88

Adversarial score matching and improved sampling for image generation

Jolicoeur-Martineau, A., Piché-Taillefer, R., Combes, R. T. D., & Mitliagkas, I. (2020) · arXiv · doi:10.48550/arxiv.2009.05475 · Cited by 88

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Guo, W., Tondi, B., & Barni, M. (2022) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2022.3190213 · Cited by 91

Black-box Detection of Backdoor Attacks with Limited Information and Data

Dong, Y., Yang, X., Deng, Z., Pang, T., Xiao, Z., Su, H., & Zhu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01617 · Cited by 92

Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning

Zhang, J., Kailkhura, B., & Han, Y.-J. (2020) · Unpublished · doi:10.13140/rg.2.2.11390.13125 · Cited by 86

Research community dynamics behind popular AI benchmarks

Martínez-Plumed, F., Barredo, P., hÉigeartaigh, S. Ó., & Hernández-Orallo, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00339-6 · Cited by 86

Self-supervised Learning is More Robust to Dataset Imbalance

Liu, H., HaoChen, J. Z., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2110.05025 · Cited by 84

Multi-Task Self-Training for Learning General Representations

Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., & Lin, T.-Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00873 · Cited by 84

A feature fusion system for basal cell carcinoma detection through data‐driven feature learning and patient profile

Kharazmi, P., Kalia, S., Lui, H., Wang, Z. J., & Lee, T. K. (2018) · Skin Research and Technology · doi:10.1111/srt.12422 · Cited by 83

PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation

Kim, J., Chang, S., & Kwak, N. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-248 · Cited by 85

Subpopulation Data Poisoning Attacks

Jagielski, M., Severi, G., Pousette Harger, N., & Oprea, A. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3485368 · Cited by 86

Adversarial Noise Layer: Regularize Neural Network by Adding Noise

You, Z., Ye, J., Li, K., Xu, Z., & Wang, P. (2019) · 2019 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2019.8803055 · Cited by 80

GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent

Daily, J., Vishnu, A., Siegel, C., Warfel, T., & Amatya, V. (2018) · arXiv · doi:10.48550/arxiv.1803.05880 · Cited by 80

On Fast Sampling of Diffusion Probabilistic Models

Kong, Z., & Ping, W. (2021) · arXiv · doi:10.48550/arxiv.2106.00132 · Cited by 79

Gossip training for deep learning

Blot, M., Picard, D., Cord, M., & Thome, N. (2016) · arXiv · doi:10.48550/arxiv.1611.09726 · Cited by 77

Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

Arazo, E., Ortego, D., Albert, P., O'Connor, N. E., & McGuinness, K. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207304 · Cited by 77

On the Accuracy of Analog Neural Network Inference Accelerators

Xiao, T. P., Feinberg, B., Bennett, C. H., Prabhakar, V., Saxena, P., Agrawal, V., Agarwal, S., & Marinella, M. J. (2022) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2022.3214409 · Cited by 76

Chain FL: Decentralized Federated Machine Learning via Blockchain

Korkmaz, C., Kocas, H. E., Uysal, A., Masry, A., Ozkasap, O., & Akgun, B. (2020) · 2020 Second International Conference on Blockchain Computing and Applications (BCCA) · doi:10.1109/bcca50787.2020.9274451 · Cited by 84

Less-Forgetful Learning for Domain Expansion in Deep Neural Networks

Jung, H., Ju, J., Jung, M., & Kim, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11769 · Cited by 75

Predictive uncertainty estimation for out-of-distribution detection in digital pathology

Linmans, J., Elfwing, S., van der Laak, J., & Litjens, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102655 · Cited by 75

Blockchain-Based Personalized Federated Learning for Internet of Medical Things

Lian, Z., Wang, W., Han, Z., & Su, C. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3279111 · Cited by 75

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv · doi:10.48550/arxiv.2107.04649 · Cited by 74

A Constructive Prediction of the Generalization Error Across Scales

Rosenfeld, J. S., Rosenfeld, A., Belinkov, Y., & Shavit, N. (2019) · arXiv · doi:10.48550/arxiv.1909.12673 · Cited by 74

A Disentangling Invertible Interpretation Network for Explaining Latent Representations

Esser, P., Rombach, R., & Ommer, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00924 · Cited by 72

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

Yang, L., Jiang, H., Cai, R., Wang, Y., Song, S., Huang, G., & Tian, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00357 · Cited by 71

Zero-Centered Fixed-Point Quantization With Iterative Retraining for Deep Convolutional Neural Network-Based Object Detectors

Kim, S., & Kim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3054879 · Cited by 71

Boosting Fast Adversarial Training With Learnable Adversarial Initialization

Jia, X., Zhang, Y., Wu, B., Wang, J., & Cao, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3184255 · Cited by 69

Semi-Supervised Contrastive Learning With Similarity Co-Calibration

Zhang, Y., Zhang, X., Li, J., Qiu, R. C., Xu, H., & Tian, Q. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3158069 · Cited by 67

Enhancing the prediction of disease-gene associations with multimodal deep learning

Luo, P., Li, Y., Tian, L.-P., & Wu, F.-X. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz155 · Cited by 67

A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation

Mohamed, E., Sirlantzis, K., & Howells, G. (2022) · Displays · doi:10.1016/j.displa.2022.102239 · Cited by 67

Searching for Low-Bit Weights in Quantized Neural Networks

Yang, Z., Wang, Y., Han, K., Xu, C., Xu, C., Tao, D., & Xu, C. (2020) · arXiv · doi:10.48550/arxiv.2009.08695 · Cited by 64

Training-free Transformer Architecture Search

Zhou, Q., Sheng, K., Zheng, X., Li, K., Sun, X., Tian, Y., Chen, J., & Ji, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01062 · Cited by 64

Neural Architecture Search Benchmarks: Insights and Survey

Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · IEEE Access · doi:10.1109/access.2023.3253818 · Cited by 64

The cascading neural network: building the Internet of Smart Things

Leroux, S., Bohez, S., De Coninck, E., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1029-1 · Cited by 59

Assessing Image Quality Issues for Real-World Problems

Chiu, T.-Y., Zhao, Y., & Gurari, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00370 · Cited by 63

Untitled work

(2018) · International Conference on Learning Representations · Cited by 62

Consistent Estimators for Learning to Defer to an Expert

Mozannar, H., & Sontag, D. (2020) · arXiv · doi:10.48550/arxiv.2006.01862 · Cited by 61

Twelve key challenges in medical machine learning and solutions

Ellis, R. J., Sander, R. M., & Limon, A. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100068 · Cited by 61

Zeno++: Robust Fully Asynchronous SGD

Xie, C., Koyejo, S., & Gupta, I. (2019) · arXiv · doi:10.48550/arxiv.1903.07020 · Cited by 60

Demystifying differentiable programming: shift/reset the penultimate backpropagator

Wang, F., Zheng, D., Decker, J., Wu, X., Essertel, G. M., & Rompf, T. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3341700 · Cited by 59

EasiEdge: A Novel Global Deep Neural Networks Pruning Method for Efficient Edge Computing

Yu, F., Cui, L., Wang, P., Han, C., Huang, R., & Huang, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3034925 · Cited by 58

Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning

Park, S., Lee, J., Mo, S., & Shin, J. (2020) · arXiv · doi:10.48550/arxiv.2002.04809 · Cited by 58

Confidence Scores Make Instance-dependent Label-noise Learning Possible

Berthon, A., Han, B., Niu, G., Liu, T., & Sugiyama, M. (2020) · arXiv · doi:10.48550/arxiv.2001.03772 · Cited by 57

Fine-grained Angular Contrastive Learning with Coarse Labels

Bukchin, G., Schwartz, E., Saenko, K., Shahar, O., Feris, R., Giryes, R., & Karlinsky, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00862 · Cited by 55

MSMatch: Semisupervised Multispectral Scene Classification With Few Labels

Gomez, P., & Meoni, G. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3126082 · Cited by 55

Revisiting Distillation and Incremental Classifier Learning

Javed, K., & Shafait, F. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-20876-9_1 · Cited by 53

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

HaoChen, J. Z., Wei, C., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2106.04156 · Cited by 53

Multi-column Deep Neural Networks for Image Classification

Cireşan, D., Meier, U., & Schmidhuber, J. (2012) · arXiv · doi:10.48550/arxiv.1202.2745 · Cited by 53

Practical and private (deep) learning without sampling or shuffling

Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., & Xu, Z. (2021) · arXiv · doi:10.48550/arxiv.2103.00039 · Cited by 52

A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing

Lo, C., Su, Y.-Y., Lee, C.-Y., & Chang, S.-C. (2017) · 2017 IEEE International Conference on Computer Design (ICCD) · doi:10.1109/iccd.2017.49 · Cited by 48

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

Cabrera, Á. A., Fu, E., Bertucci, D., Holstein, K., Talwalkar, A., Hong, J. I., & Perer, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581268 · Cited by 51

Outstanding Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks

Hirtzlin, T., Bocquet, M., Klein, J.-O., Nowak, E., Vianello, E., Portal, J.-M., & Querlioz, D. (2019) · 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas.2019.8771544 · Cited by 51

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Bengio, Y., Schölkopf, B., Wüthrich, M., & Bauer, S. (2020) · arXiv · doi:10.48550/arxiv.2010.04296 · Cited by 50

Romoa: Robust Model Aggregation for the Resistance of Federated Learning to Model Poisoning Attacks

Mao, Y., Yuan, X., Zhao, X., & Zhong, S. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-88418-5_23 · Cited by 48

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks

Malinin, A., Band, N., Ganshin, A., Chesnokov, G., Gal, Y., Gales, M. J. F., Noskov, A., Ploskonosov, A., Prokhorenkova, L., Provilkov, I., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.07455 · Cited by 48

Compressive Privacy Generative Adversarial Network

Tseng, B.-W., & Wu, P.-Y. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2968188 · Cited by 48

The Benchmark Lottery

Dehghani, M., Tay, Y., Gritsenko, A. A., Zhao, Z., Houlsby, N., Diaz, F., Metzler, D., & Vinyals, O. (2021) · arXiv · doi:10.48550/arxiv.2107.07002 · Cited by 48

COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

Lian, Z., Wang, W., & Su, C. (2021) · ICC 2021 - IEEE International Conference on Communications · doi:10.1109/icc42927.2021.9500632 · Cited by 58

Regularizing deep networks with prior knowledge: A constraint-based approach

Roychowdhury, S., Diligenti, M., & Gori, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106989 · Cited by 46

P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification

Wu, B., Zhao, S., Sun, G., Zhang, X., Su, Z., Zeng, C., & Liu, Z. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00220 · Cited by 44

Characterizing the Decision Boundary of Deep Neural Networks

Karimi, H., Derr, T., & Tang, J. (2019) · arXiv · doi:10.48550/arxiv.1912.11460 · Cited by 46

On Model Transmission Strategies in Federated Learning With Lossy Communications

Su, X., Zhou, Y., Cui, L., & Liu, J. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3240883 · Cited by 47

BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Yang, H., Duan, L., Chen, Y., & Li, H. (2021) · arXiv · doi:10.48550/arxiv.2102.10462 · Cited by 45

rTop-k: A Statistical Estimation Approach to Distributed SGD

Barnes, L. P., Inan, H. A., Isik, B., & Ozgur, A. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.3042094 · Cited by 44

Residual error based knowledge distillation

Gao, M., Wang, Y., & Wan, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.113 · Cited by 45

Predictive coding feedback results in perceived illusory contours in a recurrent neural network

Pang, Z., O’May, C. B., Choksi, B., & VanRullen, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.08.024 · Cited by 44

QKD: Quantization-aware Knowledge Distillation

Kim, J., Bhalgat, Y., Lee, J., Patel, C., & Kwak, N. (2019) · arXiv · doi:10.48550/arxiv.1911.12491 · Cited by 44

Uncertainty-Guided Semi-Supervised Few-Shot Class-Incremental Learning With Knowledge Distillation

Cui, Y., Deng, W., Xu, X., Liu, Z., Liu, Z., Pietikäinen, M., & Liu, L. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3208743 · Cited by 44

Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling

Zeng, X., Yan, M., & Zhang, M. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/3485730.3485930 · Cited by 43

Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs

Jiang, S., He, D., Yang, C., Xu, C., Luo, G., Chen, Y., Liu, Y., & Jiang, J. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8485850 · Cited by 40

Generating High Fidelity Data from Low-density Regions using Diffusion Models

Sehwag, V., Hazirbas, C., Gordo, A., Ozgenel, F., & Ferrer, C. C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01120 · Cited by 41

FLDS: An Intelligent Feature Learning Detection System for Visualizing Medical Images Supporting Fetal Four-Chamber Views

Qiao, S., Pang, S., Luo, G., Pan, S., Chen, T., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3091579 · Cited by 41

Few-Shot Class Incremental Learning Leveraging Self-Supervised Features

Ahmad, T., Dhamija, A. R., Cruz, S., Rabinowitz, R., Li, C., Jafarzadeh, M., & Boult, T. E. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00435 · Cited by 41

Temporal Coherence for Active Learning in Videos

Zolfaghari Bengar, J., Gonzalez-Garcia, A., Villalonga, G., Raducanu, B., Habibi Aghdam, H., Mozerov, M., Lopez, A. M., & van de Weijer, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00120 · Cited by 41

Multimodal Parameter-Efficient Few-Shot Class Incremental Learning

D’Alessandro, M., Alonso, A., Calabrés, E., & Galar, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw60793.2023.00364 · Cited by 40