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Incremental Learning
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Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Balancing the Past and Present: A Coordinated Replay Framework for Federated Class-Incremental Learning
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic
Class-Incremental Learning for Honey Botanical Origin Classification with Hyperspectral Images: A Study with Continual Backpropagation
Hyperbolic Dual Feature Augmentation for Open-Environment
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition
Large Language Models in the Task of Automatic Validation of Text Classifier Predictions
Improving Ad matching via Cluster-Adaptive Keyword Expansion and Relevance tuning
Teaching with Lies: Curriculum DPO on Synthetic Negatives for Hallucination Detection
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning
StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning
Listen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
Completely Weakly Supervised Class-Incremental Learning for Semantic Segmentation
Privacy-Aware Lifelong Learning
Foundation Time-Series AI Model for Realized Volatility Forecasting
SEAL: Searching Expandable Architectures for Incremental Learning
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning
PrePrompt: Predictive prompting for class incremental learning
Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models
A Federated Random Forest Solution for Secure Distributed Machine Learning
Autoencoder-Based Hybrid Replay for Class-Incremental Learning
Componential Prompt-Knowledge Alignment for Domain Incremental Learning
Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillation
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation
SetKE: Knowledge Editing for Knowledge Elements Overlap
Feature Staleness Aware Incremental Learning for CTR Prediction
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