A Deeper Look at Dataset Bias

Who is closer: A computational method for domain gap evaluation

Liu, X., & Zhang, S. S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108293 · Cited by 12

Projection-Wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis

Liu, X., Li, B., Bron, E. E., Niessen, W. J., Wolvius, E. B., & Roshchupkin, G. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87240-3_78 · Cited by 11

M-GCN: Brain-inspired memory graph convolutional network for multi-label image recognition

Yao, X. L., Xu, F., Gu, M., & Wang, P. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06803-z · Cited by 11

Underwater image filtering: methods, datasets and evaluationPreprint

Li, C. Y., Mazzon, R., & Cavallaro, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12258 · Cited by 11

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening

Isaac-Medina, B. K. S., Yücer, Ş., Bhowmik, N., & Breckon, T. P. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00059 · Cited by 10

Towards a self-sufficient face verification system

López‐López, E., Regueiro, C. V., Pardo, X. M., Franco, A., & Lumini, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114734 · Cited by 10

Machine Learning Bias in Predicting High School Grades: A Knowledge Perspective

Mendes, R., Cruz‐Jesus, F., Oliveira, T., & Castelli, M. (2021) · Emerging Science Journal · doi:10.28991/esj-2021-01298 · Cited by 10

Dataset bias exposed in face verification

López‐López, E., Pardo, X. M., Regueiro, C. V., Iglesias, R., & Casado, F. E. (2019) · IET Biometrics · doi:10.1049/iet-bmt.2018.5224 · Cited by 10

Dataset bias: A case study for visual question answering

Das, A., Anjum, S., & Gurari, D. (2019) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.7 · Cited by 10

Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?Preprint

Kortylewski, A., Egger, B., Morel-Forster, A., Schneider, A., Gerig, T., Blumer, C., Reyneke, C., & Vetter, T. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08565 · Cited by 10

A New Approach to Traffic Accident Anticipation With Geometric Features for Better Generalizability

Mahmood, F., Jeong, D., & Ryu, J. (2023) · IEEE Access · doi:10.1109/access.2023.3259992 · Cited by 9

Deep learning‐based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Finkelstein, O., Levakov, G., Kaplan, A., Zelicha, H., Meir, A. Y., Rinott, E., Tsaban, G., Witte, A. V., Blüher, M., Stümvoll, M., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26595 · Cited by 8

Discovering and Mitigating Biases in CLIP-based Image Editing

Tanjim, M. M., Singh, K. K., Kafle, K., Sinha, R., & Cottrell, G. W. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00296 · Cited by 11

Learn to Adapt for Self-Supervised Monocular Depth Estimation

Sun, Q., Yen, G. G., Tang, Y. S., & Zhao, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3289051 · Cited by 13

Why Does Data-Driven Beat Theory-Driven Computer Vision?

Tsotsos, J. K., Kotseruba, I., Andreopoulos, A., & Wu, Y. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00260 · Cited by 9

Classification of Sunflower Foliar Diseases Using Convolutional Neural Network

Dawod, R. G., & Dobre, C. (2021) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs52396.2021.00084 · Cited by 9

Co-segmentation inspired attention module for video-based computer vision tasks

Subramaniam, A., Vaidya, J., Ameen, M., Nambiar, A., & Mittal, A. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103532 · Cited by 9

Improve Model Generalization and Robustness to Dataset Bias with Bias-regularized Learning and Domain-guided AugmentationPreprint

Zhang, Y., Wu, H., Liu, H., Li, T., & Wang, M. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06745 · Cited by 11

Mitigating Sampling Bias and Improving Robustness in Active LearningPreprint

Krishnan, R., Sinha, A., Ahuja, N. A., Subedar, M., Tickoo, O., & Iyer, R. (2021) · arXiv.org · doi:10.48550/arxiv.2109.06321 · Cited by 11

Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning

Martinez, J., Nowroozilarki, Z., Jafari, R., & Mortazavi, B. J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3199199 · Cited by 8

Bridging the gap between debiasing and privacy for deep learning

Barbano, C. A., Tartaglione, E., & Grangetto, M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00424 · Cited by 8

Artificial Intelligence, Machine Learning, and Natural Language Processing

Shoenbill, K. A., Kasthurirathne, S. N., & Mendonça, E. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-29171-5_36 · Cited by 9

Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

León, L., Campos, C., & Hirzel, J. (2024) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2024.03.002 · Cited by 8

Multi-label image recognition with two-stream dynamic graph convolution networks

Cao, P., Chen, P., & Niu, Q. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104238 · Cited by 7

Machine learning approach for label-free rapid detection and identification of virus using Raman spectra

Alexander, R., Uppal, S., Dey, A., Kaushal, A., Prakash, J., & Dasgupta, K. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.10.001 · Cited by 7

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Mazaheri, P., Bidgoli, A. A., Rahnamayan, S., & Tizhoosh, H. R. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110346 · Cited by 11

Artificial Intelligence and Learning

Lee, J., Lamb, R. L., & Kim, S. (2021) · Oxford University Press eBooks · doi:10.1093/obo/9780199756810-0269 · Cited by 9

Fighting Over-Fitting with Quantization for Learning Deep Neural Networks on Noisy Labels

Tallec, G., Yvinec, E., Dapogny, A., & Bailly, K. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222687 · Cited by 6

Cross-ViT: Cross-attention Vision Transformer for Image Duplicate Detection

Chandrasiri, M. D. N., & Talagala, P. D. (2023) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr61062.2023.10382916 · Cited by 7

Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach

Fleischmann, S., Dietz, S., Shanbhag, J., Wuensch, A., Nitschke, M., Miehling, J., Wartzack, S. J., Leyendecker, S., Eskofier, B. M., & Koelewijn, A. D. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3702646 · Cited by 7

Neural Architecture Search by Estimation of Network Structure Distributions

Muravev, A., Raitoharju, J., & Gabbouj, M. (2021) · IEEE Access · doi:10.1109/access.2021.3052996 · Cited by 6

Kajian Pengaruh Dataset dan Bias Dataset terhadap Performa Akurasi Deteksi Objek

Tiyar, R. I., & Fudholi, D. H. (2021) · Petir · doi:10.33322/petir.v14i2.1350 · Cited by 6

Adaptive Adversarial Transfer Learning for Electroencephalography Classification

Tan, C., Sun, F., Zhang, W., Kong, T., Yang, C., & Zhang, X. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489116 · Cited by 6

Bias-invariant RNA-sequencing metadata annotation

Wartmann, H., Heins, S., Kloiber, K., & Bonn, S. (2021) · GigaScience · doi:10.1093/gigascience/giab064 · Cited by 4

Through a Fair Looking-Glass: Mitigating Bias in Image Datasets

Rajabi, A., Yazdani-Jahromi, M., Garibay, Ö. Ö., & Sukthankar, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_27 · Cited by 9

Detecting semantic anomalies

Ahmed, F., & Courville, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5712 · Cited by 5

Challenges of Automating Interior Construction Progress Monitoring

Zhang, Y., Chang, R., Mao, W., Zuo, J., Liu, L., & Han, Y. (2024) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-14637 · Cited by 7

Uncertainty estimation in color constancy

Buzzelli, M., & Bianco, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111175 · Cited by 5

MUTE: Inter-class Ambiguity Driven Multi-hot Target Encoding for Deep Neural Network Design

Jaiswal, M. S., Kang, B., Lee, J., & Cho, M. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00385 · Cited by 5

ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces

Vurity, A., Marasco, E., Ramachandra, R., & Park, J.-W. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00519 · Cited by 6

Decorrelation-Based Deep Learning for Bias Mitigation

Patil, P., & Purcell, K. (2022) · Future Internet · doi:10.3390/fi14040110 · Cited by 5

Towards Inclusive HRI: Using Sim2Real to Address Underrepresentation in Emotion Expression Recognition

Akhyani, S., Abbasi, M., Chen, M., & Lim, A. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982252 · Cited by 5

ATLAS - A Co-evolutionary Framework for Automatic Tuning of Adversarial Neural Networks

Shyju, S., & Murali, R. (2023) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3583133.3596376 · Cited by 5

Deep ensemble-based hard sample mining for food recognition

Nagarajan, B., Bolaños, M., Aguilar, E., & Radeva, P. I. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103905 · Cited by 5

Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms

Jahn, P., Frey, C. M. M., Beer, A., Leiber, C., & Seidl, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70368-3_1 · Cited by 7

Mitigating the Effect of Dataset Bias on Training Deep Models for Chest X-rays.Preprint

Zhang, Y., Wu, H., Liu, H., Li, T., & Wang, M. D. (2019) · arXiv (Cornell University) · Cited by 6

Deep learning pipeline for quality filtering of MRSI spectra

Rakić, M., Turco, F., Weng, G., Maes, F., Sima, D. M., & Slotboom, J. (2023) · NMR in Biomedicine · doi:10.1002/nbm.5012 · Cited by 5

On the Use of Causal Models to Build Better Datasets

Garcea, F., Morra, L., & Lamberti, F. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00225 · Cited by 5

A non-Discriminatory Approach to Ethical Deep Learning

Tartaglione, E., & Grangetto, M. (2020) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00126 · Cited by 4

Open Set Domain Adaptation for Image and Action RecognitionPreprint

Busto, P. P., Iqbal, A., & Gall, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12865 · Cited by 6

A Framework for Fairer Machine Learning in OrganizationsPreprint

Morse, L., Teodorescu, M. H., Awwad, Y., & Kane, G. C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3690570 · Cited by 5

Efficient Bias Mitigation Without Privileged Information

Zarlenga, M. E., Sankaranarayanan, S., Andrews, J. T. A., Shams, Z., Jamnik, M., & Xiang, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73220-1_9 · Cited by 5

HRI Framework for Continual Learning in Face Recognition

Belgiovine, G., Gonzalez-Billandon, J., Sciutti, A., Sandini, G., & Rea, F. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981671 · Cited by 3

De-Biasing Methods in Neural Networks: A Survey

Wang, Y., & Liu, H. (2023) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc58545.2023.10327985 · Cited by 8

JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition

Bicsi, L., Alexe, B., Ionescu, R. T., & Leordeanu, M. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00102 · Cited by 3

Dynamic Adaptation on Non-stationary Visual Domains

Shkodrani, S., Hofmann, M., & Gavves, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_12 · Cited by 3

Analytics

Kasthurirathne, S. N., & Grannis, S. J. (2022) · Springer eBooks · doi:10.1007/978-3-030-93765-2_16 · Cited by 3

Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidancePreprint

Prabhu, V. U., Yap, D. A., Wang, A., & Whaley, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12917 · Cited by 3

"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CTPreprint

Biondetti, G. P., Gauriau, R., Bridge, C. P., Lu, C., & Andriole, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08525 · Cited by 3

Understanding generative AI output with embedding models

Vargas, M., Cannon, R., Engel, A., Sarwate, A. D., & Chiang, T. (2025) · Science Advances · doi:10.1126/sciadv.adx4082 · Cited by 10

Understanding Dataset Bias in Medical Imaging: A Case Study on Chest X-rays

Dack, E., & Dai, C. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00066 · Cited by 4

Generalize Beyond the Lens: Deep Magnification Adaptation in Breast Cancer Histopathology

Shahriar, K. A., Ahmed, M., Hossain, M. S. A., Chowdhury, M. E. H., & Bhuiyan, E. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01273-4 · Cited by 2

Robust Physician Gaze Prediction Using a Deep Learning Approach

Tan, T., Montague, E. N. H., Furst, J. D., & Raicu, D. S. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00168 · Cited by 2

Improving Video Model Transfer with Dynamic Representation Learning

Li, Y., & Vasconcelos, N. M. (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/cvpr52688.2022.01868 · Cited by 2

OODA loop for learning open-world novelty problems

Yadav, P., & Kim, S. (2023) · Advances in computers · doi:10.1016/bs.adcom.2023.06.002 · Cited by 2

LLMs and Linguistic Competency: An exploration of GPT-4 and a non-hegemonic English variety

Jackson, S., Beekhuizen, B. F., Zhao, Y. C., Zhao, Z., & McEwen, R. N. (2024) · Newhouse Impact Journal · doi:10.14305/jn.29960819.2024.1.1.04 · Cited by 2

Highlights on Utilizing Machine Learning for High Performance Computing Systems

Pathak, A. R. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.358 · Cited by 2

Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

Were, M. C., Li, A., Malin, B., Yin, Z., Coco, J., Collins, B. X., Clayton, E. W., Novak, L. L., Hendricks‐Sturrup, R., Oluyomi, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/73996 · Cited by 4

The Impact of Domain Shift on Predicting Perceived Sleep Quality from Wearables

Abdalazim, N., Alchieri, L., Alecci, L., Barbiero, P., & Silvia Santini, B. (2025) · Sensors · doi:10.3390/s25134012 · Cited by 5

MiDA: Membership inference attacks against domain adaptation

Zhang, Y., Zhao, L., & Wang, Q. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.01.021 · Cited by 3

Assessing Capsule Networks with Biased Data

Ferrarini, B., Ehsan, S., Bartoli, A., Leonardis, A., & McDonald-Maier, K. D. (2019) · Open Access at Essex (University of Essex) · Cited by 2

Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks

Aboitiz, F., Legenstein, R. A., & Özdenizci, O. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44204-9_21 · Cited by 2

Underwater Object Recognition: A Domain-Adaption Methodology of Machine Learning Classifiers

Afonso, A. P. O., & Pinto, A. M. (2019) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans40490.2019.8962693 · Cited by 3

Learning Purified Feature Representations from Task-irrelevant Labels

Li, Y., Wang, C., Li, Y., Zheng, H.-T., & Shen, Y. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892229 · Cited by 2

Fairness Challenges in Artificial Intelligence

Chakrobartty, S., & El-Gayar, O. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch101 · Cited by 2

Towards A Global AI Auditing Framework: Assessment and Recommendations

(IPIE), I. P. O. T. I. E., Faveri, B., Johnson-León, M., Sylvester, P., Chun, W. H. K., Hannák, A., Mendoza, M., Broussard, M., Сергеевич, Е. Р., Nelson, A., et al. (2025) · doi:10.61452/zwed1485 · Cited by 2

Improving Balanced Accuracy for Minority Plant Species Under Data Imbalance: A Multi-Architectural Ensemble Approach

Gonzalez-Villanueva, R., & Carranza-Rojas, J. (2023) · IEEE International Conference on BioInspired Processing (BIP) · doi:10.1109/bip60195.2023.10379201 · Cited by 1

Unlearnable Examples Detection via Iterative Filtering

Yu, Y., Zheng, Q., Yang, S., Yang, W., Liu, J., Lu, S., Tan, Y., Lam, K., & Kot, A. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72359-9_18 · Cited by 2

DA 2 : Distribution-agnostic adaptive feature adaptation for one-class classification

Zhang, Z., Zhao, Z., Zhang, X., & Chen, X. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104256 · Cited by 1

From Clinical Trials to Real‐World Impact: Introducing a Computational Framework to Detect Endpoint Bias in Opioid Use Disorder Research

Odom, G., Brandt, L., Marker, A., Giorgi, S., Jainarain, G., Schwartz, H. A., Au, L., & Castro, C. (2025) · Drug and Alcohol Review · doi:10.1111/dar.70085 · Cited by 1

PVD4RCV: A Photo-realistic Multi-Distortion Video Dataset for Benchmarking and Developing Robust Computer Vision Models

Beghdadi, A., Ullah, M., Beghdadi, A., Dakkar, B.-E., Khan, Z. A., & Cheikh, F. A. (2025) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip67698.2025.11396815 · Cited by 1

Applied Profiling: Uses, Reliability and Ethics

Singh, R. (2019) · Springer eBooks · doi:10.1007/978-981-13-8403-5_10 · Cited by 1

Sequence or Pseudo-Sequence? An Analysis of Sequential Recommendation Datasets.

Woolridge, D., Wilner, S., & Glick, M. (2021) · ACM Conference on Recommender Systems (RecSys) · Cited by 1

Transfer Learning and Hyperparameter Optimization for Instance Segmentation with RGB-D Images in Reflective Elevator Environments

Reithmeier, L., Krauß, O., & Zwettler, G. A. (2021) · Computer Science Research Notes · doi:10.24132/csrn.2021.3101.30 · Cited by 1

AMAT-Net: An Unbiased Network with High Performance for Metabolic Diseases Prediction Using Facial Images

Feng, F., Wang, K., Liu, X., Gu, Q., & Wang, G. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995329 · Cited by 1

RECOMM. Measuring resilient communities: An analytical and predictive tool

Carta, S., Turchi, T., Pintacuda, L., & Jankovic, L. (2023) · International Journal of Architectural Computing · doi:10.1177/14780771231174891 · Cited by 1

MindSet: A Bias-Detection Interface Using a Visual Human-in-the-Loop Workflow

Kalananthan, S., Kichutkin, A., Shang, Z., Strausz, A., Bautiste, F. J. S., & El‐Assady, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50485-3_8 · Cited by 1

Continual Cross-Dataset Adaptation in Road Surface Classification

Cudrano, P., Bellusci, M., Macino, G., & Matteucci, M. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422521 · Cited by 1

Assessing Capsule Networks with Biased Data

Ferrarini, B., Ehsan, S., Bartoli, A., Leonardis, A., & McDonald-Maier, K. D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20205-7_8 · Cited by 3

Cognitive Relevance Transform for Population Re-Targeting

Koporec, G., Košir, A., Leonardis, A., & Perš, J. (2020) · Sensors · doi:10.3390/s20174668 · Cited by 1

Unveiling Real-Life Effects of Online Photo Sharing

Nguyen, V.-K., Popescu, A., & Deshayes-Chossart, J. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00391 · Cited by 1

An Adaptive Video-to-Video Face Identification System Based on Self-Training

López‐López, E., Regueiro, C. V., & Pardo, X. M. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412369 · Cited by 1

Deep Learning: Why Neural Networks Are State of the Art

Ganesh, A., & Urs, N. (2022) · Educational communications and technology: issues and innovations · doi:10.1007/978-3-030-84729-6_2 · Cited by 2

PENGARUH TINGKAT AKURASI DALAM IDENTIFIKASI GEJALA DAN TANDA PENYAKIT PADA TANAMAN

Mufidah, Y., Noah, R., Lawalatta, L., & Bragas, N. (2022) · Jurnal Informatika Progres · doi:10.56708/progres.v14i1.301 · Cited by 1

Adapting Convolutional Neural Networks for Geographical Domain ShiftPreprint

Ostyakov, P., & Nikolenko, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06345 · Cited by 3

Learning From Context-Agnostic Synthetic Data.Preprint

Jin, C., & Rinard, M. (2020) · arXiv (Cornell University) · Cited by 1

Bias invariant RNA-seq metadata annotationPreprint

Wartmann, H., Heins, S., Kloiber, K., & Bonn, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.26.399568 · Cited by 1

Automatic Face Understanding: Recognizing Families in PhotosPreprint

Robinson, J. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08941 · Cited by 1

Post-hoc feature-based out-of-distribution detection for real-world conditions

Wilson, S. (2024) · Queensland University of Technology · doi:10.5204/thesis.eprints.246678 · Cited by 1

Functional Connectivity Alterations in Cocaine Use Disorder: Insights from the Triple Network Model and the Addictions Neuroclinical Assessment FrameworkPreprint

Xu, Z., Li, L., Liu, R., Azzam, M., Wan, S., & Wang, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.12.623073 · Cited by 1

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

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