Papers Incremental Learning
“Incremental Learning” 태그가 달린 논문 1,491편 · 필터 해제
Hyperbolic Geometry for Open-World Object Detection in Remote Sensing Imagery
Open-world object detection (OWOD) extends closed-set detection by requiring models to identify unknown objects and incrementally learn them once annotations become available. In remote sensing imagery, object categories…
Incremental LearningObject DetectionMetric LearningIn Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models
Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological learning systems. Machine learning algorith…
Incremental LearningContinual LearningSTAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models
Deep learning models applied to medical image analysis suffer from severe catastrophic forgetting when continually adapting to new clinical tasks in dynamic environments. Mainstream incremental learning methods typically…
Incremental LearningRelative Parameter Importance in Task-Agnostic Replay-Free Continual Learning
Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learning algorithms under the constraints: (I) …
Incremental LearningText ClassificationContinual LearningText GenerationCompactly supported radial basis functions as probability density functions
Compactly Supported Radial Basis Functions (CS-RBFs) are a fundamental tool in multivariate approximation theory. However, their use in statistics and probability modeling remains underexplored, having been used mainly t…
Incremental LearningGaussian ProcessesDensity EstimationOnline Variance Reduction for Domain Adaptation on Streaming Data
This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions. Although various offline SVR algorithms for these losses have…
Incremental LearningDomain AdaptationFew-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity. This paper puts forward a relatively un…
parameter-efficient fine-tuningIncremental LearningGeneral KnowledgeTransformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS
High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be scre…
Hyperparameter OptimizationIncremental LearningActive LearningDomain-incremental audio classification using domain-specific experts and prototype classifier
This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle is that, the system is unable to access to past or future domain's data…
Incremental LearningAudio ClassificationIncremental Learning in Mirror Flows
We study mirror flows generated by a convex quadratic loss and a general convex lower semicontinuous mirror potential. We show that, when initialized near the boundary of the domain of the mirror potential, their rescale…
Incremental LearningC^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model
Universal segmentation models exhibit significant potential for diverse tasks involving different imaging modalities and segmentation objectives. Task-Incremental Learning provides a privacy-preserving approach to contin…
Incremental LearningFast and Slow Variational Continual Learning
Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural mechanism is fast and slow adaptation to …
Incremental LearningContinual LearningPhysics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity. Robust RF interference direction finding through …
Domain GeneralizationIncremental LearningCoRDE: Concept-Prior Routed Diffusion Experts for Structural Generalization in Robot Manipulation
Diffusion models excel at capturing multi-modal action distributions in robot imitation learning. However, in multi-task and long-horizon scenarios, monolithic architectures lack structural generalization capabilities, s…
Incremental LearningRobot ManipulationTask-Differentiated Atomic Skill Expansion and Routing for Continual Learning Across Highly Heterogeneous Tasks
Continual learning (CL) is commonly studied under the assumption that sequential tasks are semantically related or structurally similar. However, in highly heterogeneous settings, where tasks differ substantially in reas…
Incremental LearningContinual LearningFocus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection
Adapting Detection Transformers to Incremental Object Detection (IOD) poses a systemic challenge, as set-based optimization is inherently destabilized by sequential learning. In this work, we identify Gradient Dilution a…
Incremental LearningObject DetectionECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preserving previously acquired knowledge. Unli…
Incremental LearningText GenerationOLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons
Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that cannot adapt to dynamic real-world environments o…
Incremental LearningWeakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
Weakly Incremental Learning for Semantic Segmentation (WILSS) suffers from the continuous introduction of noisy supervision, which progressively corrupts class-level representations, leading to severe feature drift and s…
Semantic SegmentationIncremental LearningRemembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams
In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to other works, we do not attempt to avoid catastrophic forgetting, but r…
Semantic SegmentationIncremental LearningAction Recognition