Cover Image

FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feng, Qian, Tu, JiaHang, Kang, Mintong, Zhao, Hanbin, Zhang, Chao, Qian, Hui
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer
by: Feng, Qian, et al.
Published: (2024)

MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning
by: Zhao, Hanbin, et al.
Published: (2020)

CE-SDWV: Effective and Efficient Concept Erasure for Text-to-Image Diffusion Models via a Semantic-Driven Word Vocabulary
by: Tu, Jiahang, et al.
Published: (2025)

Breaking Forgetting: Training-Free Few-Shot Class-Incremental Learning via Conditional Diffusion
by: Kang, Haidong, et al.
Published: (2025)

TextToucher: Fine-Grained Text-to-Touch Generation
by: Tu, Jiahang, et al.
Published: (2024)

DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving
by: Tu, Jiahang, et al.
Published: (2024)

Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting
by: Choi, Dasol, et al.
Published: (2024)

Mass Concept Erasure in Diffusion Models with Concept Hierarchy
by: Tu, Jiahang, et al.
Published: (2026)

OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
by: Ahmed, Noor, et al.
Published: (2024)

IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting
by: Fu, Hao, et al.
Published: (2025)

LW2G: Learning Whether to Grow for Prompt-based Continual Learning
by: Feng, Qian, et al.
Published: (2024)

Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models
by: Deng, Kaiyuan, et al.
Published: (2026)

Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting
by: Kodge, Sangamesh, et al.
Published: (2023)

Neural Sinkhorn Gradient Flow
by: Zhu, Huminhao, et al.
Published: (2024)

Bias-Aware Machine Unlearning: Towards Fairer Vision Models via Controllable Forgetting
by: Aylapuram, Sai Siddhartha Chary, et al.
Published: (2025)

Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
by: Wu, Qirui, et al.
Published: (2026)

Not Just Object, But State: Compositional Incremental Learning without Forgetting
by: Zhang, Yanyi, et al.
Published: (2024)

Prompting Forgetting: Unlearning in GANs via Textual Guidance
by: Nagasubramaniam, Piyush, et al.
Published: (2025)

Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
by: Deng, Kaiyuan, et al.
Published: (2026)

To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
by: Tian, Bozhong, et al.
Published: (2024)

Concept Unlearning by Modeling Key Steps of Diffusion Process
by: Zhang, Chaoshuo, et al.
Published: (2025)

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

Defying Imbalanced Forgetting in Class Incremental Learning
by: Xu, Shixiong, et al.
Published: (2024)

Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
by: Hardan, Shahad, et al.
Published: (2025)

Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
by: Fan, Chongyu, et al.
Published: (2024)

IU: Imperceptible Universal Backdoor Attack
by: Lin, Hsin, et al.
Published: (2026)

Designing to Forget: Deep Semi-parametric Models for Unlearning
by: Zheng, Amber Yijia, et al.
Published: (2026)

Gradient Reweighting: Towards Imbalanced Class-Incremental Learning
by: He, Jiangpeng, et al.
Published: (2024)

CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models
by: Biswas, Shristi Das, et al.
Published: (2025)

Class Unlearning via Depth-Aware Removal of Forget-Specific Directions
by: Hatami, Arman, et al.
Published: (2026)

BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models
by: Wang, Fangyikang, et al.
Published: (2024)

MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning
by: Sun, Hai-Long, et al.
Published: (2024)

Don't Forget Too Much: Towards Machine Unlearning on Feature Level
by: Xu, Heng, et al.
Published: (2024)

Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning
by: Tan, Xiang, et al.
Published: (2026)

ToolFG: Towards Well-Grounded Fine-Grained Image Classification
by: Xue, Yu, et al.
Published: (2026)

Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
by: Wu, Qirui, et al.
Published: (2025)

Embedded Multi-label Feature Selection via Orthogonal Regression
by: Xu, Xueyuan, et al.
Published: (2024)

ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
by: Wu, Jiekai, et al.
Published: (2026)

Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification
by: Sun, Changchang, et al.
Published: (2024)

MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning
by: Xiong, Huiyu, et al.
Published: (2024)