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Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning
Incremental Learning (IL) aims to accumulate knowledge from sequential input tasks while
overcoming catastrophic forgetting. Existing IL methods typically assume that an incoming …
overcoming catastrophic forgetting. Existing IL methods typically assume that an incoming …
Online Continuous Generalized Category Discovery
With the advancement of deep neural networks in computer vision, artificial intelligence (AI)
is widely employed in real-world applications. However, AI still faces limitations in mimicking …
is widely employed in real-world applications. However, AI still faces limitations in mimicking …
MUNBa: Machine Unlearning via Nash Bargaining
Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while
retaining the overall utility of the model. As a multi-task learning problem, MU involves …
retaining the overall utility of the model. As a multi-task learning problem, MU involves …
[PDF][PDF] Advancing Unlearning in Generative AI: Toward Responsible Artificial General Intelligence
The rapid advances in generative artificial intelligence (GenAI) have revolutionized AI
applications, raising critical concerns regarding privacy, ethical use, and data ownership …
applications, raising critical concerns regarding privacy, ethical use, and data ownership …
No Training Data, No Cry: Model Editing without Training Data or Fine-tuning
Model Editing (ME)--such as classwise unlearning and structured pruning--is a nascent field
that deals with identifying editable components that, when modified, significantly change the …
that deals with identifying editable components that, when modified, significantly change the …