Mixup: Beyond empirical risk minimization

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259

YOLOX: Exceeding YOLO Series in 2021

Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021) · arXiv · doi:10.48550/arxiv.2107.08430 · Cited by 2,965

ViViT: A Video Vision Transformer

Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00676 · Cited by 2,767

MLP-Mixer: An all-MLP Architecture for Vision

Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2105.01601 · Cited by 2,087

Bag of Tricks for Image Classification with Convolutional Neural Networks

He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., & Li, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00065 · Cited by 1,680

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Mehta, S., & Rastegari, M. (2021) · arXiv · doi:10.48550/arxiv.2110.02178 · Cited by 1,110

Generalizing to unseen domains: A survey on domain generalization

Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046

Confident Learning: Estimating Uncertainty in Dataset Labels

Northcutt, C., Jiang, L., & Chuang, I. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12125 · Cited by 795

Mobile-Former: Bridging MobileNet and Transformer

Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., & Liu, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00520 · Cited by 731

A Comprehensive Overhaul of Feature Distillation

Heo, B., Kim, J., Yun, S., Park, H., Kwak, N., & Choi, J. Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00201 · Cited by 706

Data Augmentation Using Random Image Cropping and Patching for Deep CNNs

Takahashi, R., Matsubara, T., & Uehara, K. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2935128 · Cited by 477

Representative Forgery Mining for Fake Face Detection

Wang, C., & Deng, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01468 · Cited by 288

A Survey on Semi-, Self- and Unsupervised Learning for Image Classification

Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246

Delving into deep imbalanced regression

Yang, Y., Zha, K., Chen, Y.-C., Wang, H., & Katabi, D. (2021) · arXiv · doi:10.48550/arxiv.2102.09554 · Cited by 229

Measuring Robustness to Natural Distribution Shifts in Image Classification

Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., & Schmidt, L. (2020) · arXiv · doi:10.48550/arxiv.2007.00644 · Cited by 206

Human Uncertainty Makes Classification More Robust

Peterson, J., Battleday, R., Griffiths, T., & Russakovsky, O. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00971 · Cited by 196

Augment your batch: Improving generalization through instance repetition

Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., & Soudry, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00815 · Cited by 166

PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation

Gong, Y., Chung, Y.-A., & Glass, J. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3120633 · Cited by 164

FocalMix: Semi-Supervised Learning for 3D Medical Image Detection

Wang, D., Zhang, Y., Zhang, K., & Wang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00401 · Cited by 157

When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Chen, X., Hsieh, C.-J., & Gong, B. (2021) · arXiv · doi:10.48550/arxiv.2106.01548 · Cited by 128

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

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

Subject-Aware Contrastive Learning for Biosignals

Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020) · arXiv · doi:10.48550/arxiv.2007.04871 · Cited by 74

MultiGrain: a unified image embedding for classes and instances

Berman, M., Jégou, H., Vedaldi, A., Kokkinos, I., & Douze, M. (2019) · arXiv · doi:10.48550/arxiv.1902.05509 · Cited by 69

Interpolation-based Semi-supervised Learning for Object Detection

Jeong, J., Verma, V., Hyun, M., Kannala, J., & Kwak, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01143 · Cited by 66

Understanding the Failure Modes of Out-of-Distribution Generalization

Nagarajan, V., Andreassen, A., & Neyshabur, B. (2020) · arXiv · doi:10.48550/arxiv.2010.15775 · Cited by 55

Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

Kong, L., Jiang, H., Zhuang, Y., Lyu, J., Zhao, T., & Zhang, C. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.102 · Cited by 58

Differentiation reveals latent features of aging and an energy barrier in murine myogenesis

Kimmel, J. C., Yi, N., Roy, M., Hendrickson, D. G., & Kelley, D. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109046 · Cited by 42

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25

Towards Recognizing Unseen Categories in Unseen Domains

Mancini, M., Akata, Z., Ricci, E., & Caputo, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58592-1_28 · Cited by 9

Untitled work

(2021) · doi:10.4230/lipics.giscience.2023.35