SOTAVerified

Continual Learning

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

Papers

Showing 12511300 of 2644 papers

TitleStatusHype
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams0
COBRA: A Continual Learning Approach to Vision-Brain Understanding0
Deep Lidar-guided Image Deblurring0
Deep learning via message passing algorithms based on belief propagation0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning0
Deep Generative Dual Memory Network for Continual Learning0
Deep evolving semi-supervised anomaly detection0
A Survey on Deep Industrial Transfer Learning in Fault Prognostics0
Advancing Continual Learning for Robust Deepfake Audio Classification0
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems0
CMT: A Memory Compression Method for Continual Knowledge Learning of Large Language Models0
Federated Continual Learning for Socially Aware Robotics0
Decentralised Emergence of Robust and Adaptive Linguistic Conventions in Populations of Autonomous Agents Grounded in Continuous Worlds0
CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning0
Debugging using Orthogonal Gradient Descent0
CM2-Net: Continual Cross-Modal Mapping Network for Driver Action Recognition0
A Survey on Dataset Distillation: Approaches, Applications and Future Directions0
DDFAD: Dataset Distillation Framework for Audio Data0
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning0
Clustering-based Domain-Incremental Learning0
CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization0
Advances and Challenges in Meta-Learning: A Technical Review0
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View0
Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding Confusion0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data0
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning0
Data-dependent and Oracle Bounds on Forgetting in Continual Learning0
CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update0
A Survey of Continual Reinforcement Learning0
Closed-Loop Memory GAN for Continual Learning0
Data Augmented Flatness-aware Gradient Projection for Continual Learning0
Dark Experience for Incremental Keyword Spotting0
Closed-loop Control for Online Continual Learning0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools0
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models0
Closed-form merging of parameter-efficient modules for Federated Continual Learning0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning0
A study on the plasticity of neural networks0
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Models0
Directions of Curvature as an Explanation for Loss of Plasticity0
CLOPS: Continual Learning of Physiological Signals0
A Study on Efficiency in Continual Learning Inspired by Human Learning0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review0
CSG0: Continual Urban Scene Generation with Zero Forgetting0
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering0
Continual Learning Under Language Shift0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Multi-task Learning (MTL; Upper Bound)F1 - macro0.88Unverified
2CTRF1 - macro0.84Unverified
3B-CLF1 - macro0.81Unverified
4LAMOLF1 - macro0.81Unverified
5OWMF1 - macro0.79Unverified
6A-GEMF1 - macro0.78Unverified
7HATF1 - macro0.78Unverified
8Independent Learning (ONE)F1 - macro0.78Unverified
9KANF1 - macro0.77Unverified
10Naive Continual Learning (NCL)F1 - macro0.77Unverified
#ModelMetricClaimedVerifiedStatus
1NetTailordecathlon discipline (Score)3,744Unverified
2Depthwise Soft Sharingdecathlon discipline (Score)3,507Unverified
3Parallel Res. adapt.decathlon discipline (Score)3,412Unverified
4Depthwise Sharingdecathlon discipline (Score)3,234Unverified
5Series Res. adapt.decathlon discipline (Score)3,159Unverified
6Res. adapt. (large)decathlon discipline (Score)3,131Unverified
7DANdecathlon discipline (Score)2,851Unverified
8Piggybackdecathlon discipline (Score)2,838Unverified
9Res. adapt. finetune alldecathlon discipline (Score)2,643Unverified
10Res. adapt. decaydecathlon discipline (Score)2,621Unverified
#ModelMetricClaimedVerifiedStatus
1Model Zoo-ContinualAverage Accuracy94.99Unverified
2ALTAAverage Accuracy92.98Unverified
3kNN-CLIPAverage Accuracy90.8Unverified
4RMNAverage Accuracy81Unverified
5CPGAverage Accuracy80.9Unverified
6CondConvContinualAverage Accuracy77.4Unverified
7PAENetAverage Accuracy77.1Unverified
8CPG-lightAverage Accuracy77Unverified
9PackNetAverage Accuracy67.5Unverified
#ModelMetricClaimedVerifiedStatus
1ALTA-ViTB/16Average Accuracy89.8Unverified
2ALTA-RN50x4Average Accuracy84.73Unverified
3ALTA-RN101Average Accuracy83.35Unverified
4ALTA-RN50Average Accuracy81.07Unverified
5SNCLAverage Accuracy52.85Unverified
6DER [buzzega2020dark]Average Accuracy51.78Unverified
7ER[riemer2018learning]Average Accuracy48.64Unverified
8iCaRL [rebuffi2017icarl]Average Accuracy31.55Unverified
9A-GEM [chaudhry2018efficient]Average Accuracy25.33Unverified
#ModelMetricClaimedVerifiedStatus
1CAT (CNN backbone)Acc0.76Unverified
2CAT (MLP backbone)Acc0.69Unverified
3EWCAcc0.65Unverified
4HyperNetAcc0.6Unverified
5PathNetAcc0.58Unverified
6HATAcc0.57Unverified
7RPSNetAcc0.55Unverified
#ModelMetricClaimedVerifiedStatus
1CTRF1 - macro0.95Unverified
2HATF1 - macro0.95Unverified
3CATF1 - macro0.95Unverified
4B-CLF1 - macro0.95Unverified
5EWCF1 - macro0.92Unverified
6LAMOLF1 - macro0.46Unverified
#ModelMetricClaimedVerifiedStatus
1CondConvContinualAccuracy84.26Unverified
2H$^{2}$Accuracy84.1Unverified
3CPGAccuracy83.59Unverified
4PiggybackAccuracy80.5Unverified
5PackNetAccuracy80.41Unverified
6ProgressiveNetAccuracy78.94Unverified
#ModelMetricClaimedVerifiedStatus
1CTRF1 - macro0.89Unverified
2CATF1 - macro0.87Unverified
3HATF1 - macro0.86Unverified
4KANF1 - macro0.81Unverified
5B-CLF1 - macro0.77Unverified
6EWCF1 - macro0.66Unverified
#ModelMetricClaimedVerifiedStatus
1CondConvContinualAccuracy97.16Unverified
2CPGAccuracy96.62Unverified
3H$^{2}$Accuracy94.9Unverified
4PiggybackAccuracy94.77Unverified
5ProgressiveNetAccuracy93.41Unverified
6PackNetAccuracy93.04Unverified
#ModelMetricClaimedVerifiedStatus
1PiggybackAccuracy76.16Unverified
2ProgressiveNetAccuracy76.16Unverified
3CondConvContinualAccuracy76.16Unverified
4CPGAccuracy75.81Unverified
5PackNetAccuracy75.71Unverified
6H$^{2}$Accuracy75.71Unverified
#ModelMetricClaimedVerifiedStatus
1CondConvContinualAccuracy80.77Unverified
2CPGAccuracy80.33Unverified
3PiggybackAccuracy79.91Unverified
4ProgressiveNetAccuracy76.35Unverified
5H$^{2}$Accuracy76.2Unverified
6PackNetAccuracy76.17Unverified
#ModelMetricClaimedVerifiedStatus
1CPGAccuracy92.8Unverified
2CondConvContinualAccuracy92.61Unverified
3H$^{2}$Accuracy90.6Unverified
4PiggybackAccuracy89.62Unverified
5ProgressiveNetAccuracy89.21Unverified
6PackNetAccuracy86.11Unverified
#ModelMetricClaimedVerifiedStatus
1CondConvContinualAccuracy78.32Unverified
2CPGAccuracy77.15Unverified
3H$^{2}$Accuracy75.1Unverified
4ProgressiveNetAccuracy74.94Unverified
5PiggybackAccuracy71.33Unverified
6PackNetAccuracy69.4Unverified
#ModelMetricClaimedVerifiedStatus
1ALTA-ViTB/16Average Accuracy92.85Unverified
2ALTA-RN50x4Average Accuracy84.91Unverified
3RMN (Resnet)Average Accuracy84.9Unverified
4ALTA-RN101Average Accuracy84.77Unverified
5ALTA-RN50Average Accuracy83.87Unverified
#ModelMetricClaimedVerifiedStatus
1RMNAccuracy68.1Unverified
2CondConvContinualAccuracy61.32Unverified
3CCGNAccuracy35.24Unverified
4DGMwAccuracy17.82Unverified
5DGMaAccuracy15.16Unverified
#ModelMetricClaimedVerifiedStatus
1RMNAverage Accuracy97.99Unverified
2Model Zoo-ContinualAverage Accuracy97.71Unverified
3CODE-CLAverage Accuracy96.56Unverified
#ModelMetricClaimedVerifiedStatus
1TAG-RMSPropAccuracy62.59Unverified
#ModelMetricClaimedVerifiedStatus
1CODE-CLAverage Accuracy93.32Unverified
#ModelMetricClaimedVerifiedStatus
1TEST1:3 Accuracy2Unverified
#ModelMetricClaimedVerifiedStatus
1TAG-RMSPropAverage Accuracy62.79Unverified
#ModelMetricClaimedVerifiedStatus
1IBMAccuracy82.69Unverified
#ModelMetricClaimedVerifiedStatus
1IBMAccuracy88.15Unverified
#ModelMetricClaimedVerifiedStatus
1Model Zoo-ContinualAverage Accuracy84.27Unverified
#ModelMetricClaimedVerifiedStatus
1TAG-RMSPropAccuracy61.58Unverified
#ModelMetricClaimedVerifiedStatus
1CODE-CLAverage Accuracy68.83Unverified
#ModelMetricClaimedVerifiedStatus
1TAG-RMSPropAccuracy57.2Unverified
#ModelMetricClaimedVerifiedStatus
1IBMAccuracy53.9Unverified
#ModelMetricClaimedVerifiedStatus
1MRMAcc78.4Unverified
#ModelMetricClaimedVerifiedStatus
1Model Zoo-ContinualAverage Accuracy99.66Unverified
#ModelMetricClaimedVerifiedStatus
1CODE-CLAverage Accuracy77.21Unverified
#ModelMetricClaimedVerifiedStatus
1H$^{2}$Top 1 Accuracy %97.3Unverified
#ModelMetricClaimedVerifiedStatus
1H$^{2}$Top 1 Accuracy %99.9Unverified
#ModelMetricClaimedVerifiedStatus
1IBMAccuracy52.38Unverified