A Deeper Look at Dataset Bias

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

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

Zhang, Y., David, P., & Gong, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.223 · Cited by 514

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

Wang, T., Zhao, J., Yatskar, M., Chang, K.-W., & Ordóñez, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00541 · Cited by 353

Computer vision applications in construction: Current state, opportunities & challenges

Paneru, S., & Jeelani, I. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103940 · Cited by 332

Attention-Driven Dynamic Graph Convolutional Network for Multi-label Image Recognition

Ye, J., He, J., Peng, X., Wu, W., & Qiao, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_39 · Cited by 251

Detection and profiling of dataset bias in CNN analysis of images

Martínez, E., Okonoda, M., Dalal, A., & Shamir, L. (2026) · Computing in Science & Engineering · doi:10.1109/mcse.2026.3675925

Examining CNN Representations With Respect to Dataset Bias

Zhang, Q., Wang, W., & Zhu, S. C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11833 · Cited by 95

Identifying Bias in Deep Neural Networks Using Image Transforms

Erukude, S. T., Joshi, A., & Shamir, L. (2024) · Computers · doi:10.3390/computers13120341 · Cited by 13

A Testbed for Cross-Dataset AnalysisPreprint

Tommasi, T., Tuytelaars, T., & Barbara Caputo, B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.5923 · Cited by 50

A Conservative Approach for Unbiased Learning on Unknown Biases

Jeon, M., Kim, D., Lee, W., Kang, M., & Lee, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01625 · Cited by 13

Observing Pre-Trained Convolutional Neural Network (CNN) Layers as Feature Extractor for Detecting Bias in Image Classification Data

Ardison, A. C. I., Hutagalung, M. J. R., Chernando, R., & Cenggoro, T. W. (2022) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v16i2.8144 · Cited by 5

Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks

Dhar, S., & Shamir, L. (2021) · Visual Informatics · doi:10.1016/j.visinf.2021.10.001 · Cited by 36

A survey on bias in visual datasets

Fabbrizzi, S., Papadopoulos, S., Ntoutsi, E., & Kompatsiaris, I. Y. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103552 · Cited by 164

Visual Recognition with Deep Learning from Biased Image Datasets.Preprint

Vogel, R., Clémençon, S., & Laforgue, P. (2021) · arXiv (Cornell University)

A Survey on Image Datasets for Computer Vision and Deep Learning Convolutional Neural Network Tasks

Aggarwal, B., Acharya, A., & Laghari, A. (2020) · doi:10.13140/rg.2.2.22391.44966 · Cited by 2