Papers Few-Shot Class-Incremental Learning
“Few-Shot Class-Incremental Learning” 태그가 달린 논문 143편 · 필터 해제
Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition
The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forge…
Few-Shot Class-Incremental LearningSelf-Supervised LearningMalware ClassificationContinual LearningOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
Few-shot class-incremental learning (FSCIL) in synthetic aperture radar imagery presents unique challenges due to severe data scarcity and SAR-specific variability. In particular, strong azimuth sensitivity in SAR induce…
Few-Shot Class-Incremental LearningClass Incremental LearningHyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning
Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods assume homogeneous domains and balanced data distributions, limiting…
Few-Shot Class-Incremental LearningContinual LearningWhen Sensing Varies with Contexts: Context-as-Transform for Tactile Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) can be particularly susceptible to acquisition contexts with only a few labeled samples. A typical scenario is tactile sensing, where the acquisition context ({\it e.g.}, diver…
Few-Shot Class-Incremental LearningSPRINT: Semi-supervised Prototypical Representation for Few-Shot Class-Incremental Tabular Learning
Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-Incremental Learning (FSCIL) is established in computer vision, its ap…
Few-Shot Class-Incremental LearningUnlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples while preserving previously acquired knowledge. Traditional methods often utilize a frozen pre-trained feature…
Few-Shot Class-Incremental LearningFeature-Space Generative Models for One-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing novel classes with limited data. We focus o…
Few-Shot Class-Incremental LearningCASP: Few-Shot Class-Incremental Learning with CLS Token Attention Steering Prompts
Few-shot class-incremental learning (FSCIL) presents a core challenge in continual learning, requiring models to rapidly adapt to new classes with very limited samples while mitigating catastrophic forgetting. Recent pro…
Few-Shot Class-Incremental LearningContinual LearningDivide and Conquer: Static-Dynamic Collaboration for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to continuously recognize novel classes under limited data, which suffers from the key stability-plasticity dilemma: balancing the retention of old knowledge with the acqu…
Few-Shot Class-Incremental LearningPKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting and overfitting to new classes. Existing …
Few-Shot Class-Incremental LearningCD^2: Constrained Dataset Distillation for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) receives significant attention from the public to perform classification continuously with a few training samples, which suffers from the key catastrophic forgetting problem. E…
Few-Shot Class-Incremental LearningFew-shot Class-Incremental Learning via Generative Co-Memory Regularization
Few-shot class-incremental learning (FSCIL) aims to incrementally learn models from a small amount of novel data, which requires strong representation and adaptation ability of models learned under few-example supervisio…
Few-Shot Class-Incremental LearningDomain AdaptationOpen Horizons: Evaluating Deep Models in the Wild
Open-world deployment requires models to recognize both known categories and remain reliable when novel classes appear. We present a unified experimental study spanning open-set recognition (OSR) and few-shot class-incre…
Few-Shot Class-Incremental LearningAutomatic Attack Discovery for Few-Shot Class-Incremental Learning via Large Language Models
Few-shot class incremental learning (FSCIL) is a more realistic and challenging paradigm in continual learning to incrementally learn unseen classes and overcome catastrophic forgetting on base classes with only a few tr…
Few-Shot Class-Incremental LearningClass Incremental LearningReinforcement LearningContinual LearningBreaking Forgetting: Training-Free Few-Shot Class-Incremental Learning via Conditional Diffusion
Efforts to overcome catastrophic forgetting in Few-Shot Class-Incremental Learning (FSCIL) have primarily focused on developing more effective gradient-based optimization strategies. In contrast, little attention has bee…
Few-Shot Class-Incremental LearningDiffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
Few-Shot Class-Incremental Learning (FSCIL) challenges models to sequentially learn new classes from minimal examples without forgetting prior knowledge, a task complicated by the stability-plasticity dilemma and data sc…
Few-Shot Class-Incremental LearningData AugmentationImage GenerationSAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network
Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments. However, the scenario becomes particularly challenging when data sample…
Few-Shot Class-Incremental LearningMoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) must contend with the dual challenge of learning new classes from scarce samples while preserving old class knowledge. Existing methods use the frozen feature extractor and cla…
Few-Shot Class-Incremental LearningContrastive LearningHyperbolic Coarse-to-Fine Few-Shot Class-Incremental Learning
In the field of machine learning, hyperbolic space demonstrates superior representation capabilities for hierarchical data compared to conventional Euclidean space. This work focuses on the Coarse-To-Fine Few-Shot Class-…
Few-Shot Class-Incremental LearningSeeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification
The rapid growth of 3D digital content necessitates expandable recognition systems for open-world scenarios. However, existing 3D class-incremental learning methods struggle under extreme data scarcity due to geometric m…
Few-Shot Class-Incremental Learning