Papers class-incremental learning
“class-incremental learning” 태그가 달린 논문 707편 · 필터 해제
Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models need to continually adapt to newly emerging 3D object categories…
class-incremental learningAutonomous DrivingPoint CloudsKnowing Beyond the Known: Reinforced Knowledge Specification for Multi-Label Class-Incremental Learning
Existing class-incremental learning methods struggle in multi-label scenarios (MLCIL) due to the inherent contradiction of learning objectives arising from co-occurring and incomplete labels. We argue that the core obsta…
class-incremental learningGeo-LoRA: Geometry-Aware Subspace Evolution for Low-Rank Adaptation in Continual Learning
Rehearsal-free class-incremental learning (CIL) with LoRA adapters remains challenging because the low-rank subspaces updated across tasks evolve without geometric control, causing unstable shared representations and rep…
class-incremental learningContinual LearningParameter Efficient Continual Learning for Sparse Event-Based Transformers
Robotic and edge intelligence systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previously learned knowledge under strict memory and energy constraints. W…
parameter-efficient fine-tuningclass-incremental learningContinual LearningEvent-based visionSPARCL: Spectral Partitioned Analytic Continual Learning
Analytic continual learning has emerged as a strong exemplar-free alternative to gradient-based class-incremental learning because it replaces iterative optimization with closed-form ridge updates. Yet the usual forgetti…
class-incremental learningContinual LearningSocialized Division and Collaboration: Rethinking Class-Incremental Learning under Optimization Conflicts
Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sessions. While effective under mild distributional shifts, this formula…
class-incremental learningContinual LearningBreaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning
Class-incremental learning (CIL) requires models to continuously acquire new knowledge while avoiding catastrophic forgetting. While exemplar replay is effective, it raises concerns regarding privacy and storage. Thus, g…
class-incremental learningClass Incremental LearningSpectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions
Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursive Least Squares (RLS) and have achieved the state-of-the-art results …
class-incremental learningComputational EfficiencyContinual LearningREBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation
Training-free in-context segmentation enables new object categories to be introduced at inference time from a single annotated reference image, eliminating the retraining and memory overhead of class-incremental learning…
class-incremental learningSemantic correspondenceDRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our analyses suggest that classification supervision alone does not sufficien…
class-incremental learningPrototype Latent World Model Replay for Class-Incremental Learning
Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones. This is difficult when raw old samples are no longer available. We propose Prototype Latent World Model Rep…
class-incremental learningFisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenarios introduces unique challenges: 1) Capacity conflict and catastrophic …
class-incremental learningClass Incremental LearningFederated LearningData-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning
Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task. Existing cold-start methods typically either train the backbon…
class-incremental learningImage ClassificationContinual LearningGeometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning
Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space. While maintaining class-conditional Gaussian statistics provides a principled classification strategy,…
class-incremental learningWhen Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
Federated class-incremental learning (FCIL) becomes substantially harder when clients observe different label subsets, progress through tasks at different stages, and provide uneven supervision for the same semantic conc…
class-incremental learningListen, Look, and Learn: Learning Without Forgetting through SAM-Audio
Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. While recent CIL advances have spurred significant interest across various modalities, the audio-v…
class-incremental learningHydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers
We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedded and resource-constrained environments. While most Class-Incremental…
class-incremental learningContinual LearningTwo-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning
Continual learning (CL) seeks models that acquire new skills without erasing prior knowledge. In exemplar-free class-incremental learning (EFCIL), this challenge is amplified because past data cannot be stored, making re…
class-incremental learningContinual LearningRevisiting Prototype Rehearsal for Exemplar-Free Continual Learning: Manifold-Aware Boundary Sampling with Adaptive Class-Balanced Loss
Exemplar-free class-incremental learning (EFCIL) aims to acquire new classes over time without storing raw data. Historically, prototype rehearsal, which samples around stored class prototypes and mixes them with current…
class-incremental learningContinual LearningBeyond Classification: Dynamic Adapter Routing for Continual Multimodal Retrieval
While retrieval is a core function of vision-language models, continually updating these models for retrieval tasks remains critically underexplored. Existing work often approaches continual retrieval through the lens of…
class-incremental learning