Imagen de Portada

Gradient Extrapolation for Debiased Representation Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Asaad, Ihab, Shadaydeh, Maha, Denzler, Joachim
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Mitigating Spurious Correlations in Patch-wise Tumor Classification on High-Resolution Multimodal Images
por: Asaad, Ihab, et al.
Publicado: (2025)

Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients
por: Penzel, Niklas, et al.
Publicado: (2025)

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models
por: Venkataramanan, Aishwarya, et al.
Publicado: (2025)

Realistic Face Reconstruction from Facial Embeddings via Diffusion Models
por: Han, Dong, et al.
Publicado: (2026)

Deep Learning-based Group Causal Inference in Multivariate Time-series
por: Ahmad, Wasim, et al.
Publicado: (2024)

Group Interventions on Deep Networks for Causal Discovery in Subsystems
por: Ahmad, Wasim, et al.
Publicado: (2025)

Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
por: Hong, Jinyung, et al.
Publicado: (2024)

Embracing the black box: Heading towards foundation models for causal discovery from time series data
por: Stein, Gideon, et al.
Publicado: (2024)

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
por: Schmalwasser, Laines, et al.
Publicado: (2025)

Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition
por: Peng, Pei, et al.
Publicado: (2025)

ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
por: Bourriez, Nicolas, et al.
Publicado: (2023)

Deep Learning for BioImaging: What Are We Learning?
por: Svatko, Ivan, et al.
Publicado: (2026)

Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model
por: Huang, Lin-Chun, et al.
Publicado: (2025)

Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations
por: Ratzlaff, Neale, et al.
Publicado: (2024)

Navigate Beyond Shortcuts: Debiased Learning through the Lens of Neural Collapse
por: Wang, Yining, et al.
Publicado: (2024)

Robust Emotion Recognition in Context Debiasing
por: Yang, Dingkang, et al.
Publicado: (2024)

Model Debiasing by Learnable Data Augmentation
por: Morerio, Pietro, et al.
Publicado: (2024)

DeNetDM: Debiasing by Network Depth Modulation
por: Sreelatha, Silpa Vadakkeeveetil, et al.
Publicado: (2024)

Debiasing Vison-Language Models with Text-Only Training
por: Yang, Yunfan, et al.
Publicado: (2024)

Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing
por: Fang, Zihan, et al.
Publicado: (2025)

Learning Ensembles of Vision-based Safety Control Filters
por: Tabbara, Ihab, et al.
Publicado: (2024)

Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations
por: Shi, Kexuan, et al.
Publicado: (2024)

Towards Real-world Debiasing: Rethinking Evaluation, Challenge, and Solution
por: Kuang, Peng, et al.
Publicado: (2024)

BendVLM: Test-Time Debiasing of Vision-Language Embeddings
por: Gerych, Walter, et al.
Publicado: (2024)

CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
por: Wu, Chengfeng, et al.
Publicado: (2026)

Prototypical Distillation and Debiased Tuning for Black-box Unsupervised Domain Adaptation
por: Liang, Jian, et al.
Publicado: (2024)

Training Without Orthogonalization, Inference With SVD: A Gradient Analysis of Rotation Representations
por: Choy, Chris
Publicado: (2026)

FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs
por: Dehdashtian, Sepehr, et al.
Publicado: (2024)

A Learning Paradigm for Interpretable Gradients
por: Figueroa, Felipe Torres, et al.
Publicado: (2024)

Learning to Predict Gradients for Semi-Supervised Continual Learning
por: Luo, Yan, et al.
Publicado: (2022)

Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models
por: Peng, Bo, et al.
Publicado: (2026)

Matryoshka Representation Learning
por: Kusupati, Aditya, et al.
Publicado: (2022)

Random Representations Outperform Online Continually Learned Representations
por: Prabhu, Ameya, et al.
Publicado: (2024)

Personalized Generative Models for Contextual Debiasing
por: Liang, Xinran, et al.
Publicado: (2026)

Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation
por: Zhang, Jiawei, et al.
Publicado: (2025)

Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
por: Shilova, Veronika, et al.
Publicado: (2025)

Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning
por: Ballas, Aristotelis, et al.
Publicado: (2026)

Learning Color Equivariant Representations
por: Yang, Yulong, et al.
Publicado: (2024)

Denoising Autoregressive Representation Learning
por: Li, Yazhe, et al.
Publicado: (2024)

Diffusion-Based Representation Learning
por: Mittal, Sarthak, et al.
Publicado: (2021)