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Continual Learning
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Continual Learning (also known as Incremental Learning, Life-long Learning) is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available anymore during training new ones.
If not mentioned, the benchmarks here are Task-CL, where task-id is provided on validation.
Source:
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Three scenarios for continual learning
Lifelong Machine Learning
Continual lifelong learning with neural networks: A review
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis
Information-Theoretic Generalization Bounds of Replay-based Continual Learning
RegCL: Continual Adaptation of Segment Anything Model via Model Merging
PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
Overcoming catastrophic forgetting in neural networks
Continual Reinforcement Learning by Planning with Online World Models
The Bayesian Approach to Continual Learning: An Overview
Balancing the Past and Present: A Coordinated Replay Framework for Federated Class-Incremental Learning
Rethinking Query-based Transformer for Continual Image Segmentation
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate
Temporal Continual Learning with Prior Compensation for Human Motion Prediction
Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual Learning
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
Continual Gradient Low-Rank Projection Fine-Tuning for LLMs
A Survey of Continual Reinforcement Learning
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
Little By Little: Continual Learning via Self-Activated Sparse Mixture-of-Rank Adaptive Learning
CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
The Importance of Being Lazy: Scaling Limits of Continual Learning
Weight Factorization and Centralization for Continual Learning in Speech Recognition
A Comprehensive Survey on Continual Learning in Generative Models
Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning
SWE-Bench-CL: Continual Learning for Coding Agents
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Class-Incremental Learning for Honey Botanical Origin Classification with Hyperspectral Images: A Study with Continual Backpropagation
Saturation Self-Organizing Map
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