Copertina

Explainable Metric Learning for Deflating Data Bias

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Andrews, Emma, Mishra, Prabhat
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning
di: Pan, Zhixin, et al.
Pubblicazione: (2024)

Utilizing Transfer Learning and pre-trained Models for Effective Forest Fire Detection: A Case Study of Uttarakhand
di: Gupta, Hari Prabhat, et al.
Pubblicazione: (2024)

Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection (extended version)
di: Cramer, Emma, et al.
Pubblicazione: (2023)

Explainable YOLO-Based Dyslexia Detection in Synthetic Handwriting Data
di: Fink, Nora
Pubblicazione: (2025)

Does Data-Efficient Generalization Exacerbate Bias in Foundation Models?
di: Queiroz, Dilermando, et al.
Pubblicazione: (2024)

Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data
di: Shi, Yucheng, et al.
Pubblicazione: (2025)

Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning
di: Basheer, Ramzan, et al.
Pubblicazione: (2024)

Disentangled Representation Learning via Modular Compositional Bias
di: Jung, Whie, et al.
Pubblicazione: (2025)

Melanoma Classification Through Deep Ensemble Learning and Explainable AI
di: Perera, Wadduwage Shanika, et al.
Pubblicazione: (2025)

Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview
di: Chaddad, Ahmad, et al.
Pubblicazione: (2025)

Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
di: Vepa, Arvind Murari, et al.
Pubblicazione: (2024)

Unveiling the Backbone-Optimizer Coupling Bias in Visual Representation Learning
di: Li, Siyuan, et al.
Pubblicazione: (2024)

Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning
di: Sinha, Sanchit, et al.
Pubblicazione: (2025)

BM-CL: Bias Mitigation through the lens of Continual Learning
di: Mansilla, Lucas, et al.
Pubblicazione: (2025)

Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
di: Matos, Ivan Luiz De Moura, et al.
Pubblicazione: (2026)

Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis
di: Kanamugire, Olivier, et al.
Pubblicazione: (2026)

R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning
di: Zhao, Jiaxing, et al.
Pubblicazione: (2025)

Universal Bovine Identification via Depth Data and Deep Metric Learning
di: Sharma, Asheesh, et al.
Pubblicazione: (2024)

PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation
di: Kim, Kwonyoung, et al.
Pubblicazione: (2022)

Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias
di: Kwon, JuneHyoung, et al.
Pubblicazione: (2026)

Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
di: Ren, Li, et al.
Pubblicazione: (2024)

Clean-GS: Semantic Mask-Guided Pruning for 3D Gaussian Splatting
di: Mishra, Subhankar
Pubblicazione: (2026)

How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
di: Roos, Nathan, et al.
Pubblicazione: (2025)

What's Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold
di: Azeglio, Simone, et al.
Pubblicazione: (2025)

Leveraging Data to Say No: Memory Augmented Plug-and-Play Selective Prediction
di: Sarkar, Aditya, et al.
Pubblicazione: (2026)

Learning Unified Distance Metric Across Diverse Data Distributions with Parameter-Efficient Transfer Learning
di: Kim, Sungyeon, et al.
Pubblicazione: (2023)

OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
di: Wang, Xiangming, et al.
Pubblicazione: (2024)

Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation
di: Akkaya, Ibrahim Batuhan, et al.
Pubblicazione: (2022)

Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
di: Zhu, Yuehua, et al.
Pubblicazione: (2020)

CosFairNet:A Parameter-Space based Approach for Bias Free Learning
di: Dwivedi, Rajeev Ranjan, et al.
Pubblicazione: (2024)

PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning
di: Zhu, Fei, et al.
Pubblicazione: (2024)

On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process
di: Iwase, Shun, et al.
Pubblicazione: (2025)

Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations
di: Taparia, Aditya, et al.
Pubblicazione: (2024)

Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis
di: Kudo, Shoma, et al.
Pubblicazione: (2024)

DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning
di: Gebrerufael, Hadush Hailu, et al.
Pubblicazione: (2024)

Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions
di: Parmar, Mihir, et al.
Pubblicazione: (2022)

From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning
di: Zhang, Ji, et al.
Pubblicazione: (2023)

A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
di: Cao, Chengtai, et al.
Pubblicazione: (2022)

Fast and Simple Explainability for Point Cloud Networks
di: Levi, Meir Yossef, et al.
Pubblicazione: (2024)

Improved Segmentation of Polyps and Visual Explainability Analysis
di: Asare, Akwasi, et al.
Pubblicazione: (2025)