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 26012644 of 2644 papers

TitleStatusHype
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning0
Unlocking Robust Segmentation Across All Age Groups via Continual Learning0
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks0
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach0
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms0
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling0
Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of Discretization0
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology0
Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse0
Enhancing Visual Continual Learning with Language-Guided Supervision0
Ensembles and Encoders for Task-Free Continual Learning0
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer0
Simplex-enabled Safe Continual Learning Machine0
ER-FSL: Experience Replay with Feature Subspace Learning for Online Continual Learning0
Simplified priors for Object-Centric Learning0
EsaCL: Efficient Continual Learning of Sparse Models0
Continual Invariant Risk Minimization0
Evaluating and Improving Continual Learning in Spoken Language Understanding0
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments0
Evaluating Continual Learning on a Home Robot0
Continual Instruction Tuning for Large Multimodal Models0
Evaluating Pretrained models for Deployable Lifelong Learning0
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR0
Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints0
Evolving Dictionary Representation for Few-shot Class-incremental Learning0
EXACFS -- A CIL Method to mitigate Catastrophic Forgetting0
Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition0
Single-Net Continual Learning with Progressive Segmented Training (PST)0
Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?0
Exemplar-condensed Federated Class-incremental Learning0
Exemplar-Free Continual Learning for State Space Models0
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling0
Exemplar-Free Continual Transformer with Convolutions0
Exemplar-free Online Continual Learning0
Data-Incremental Continual Offline Reinforcement Learning0
Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning0
Experience Replay Addresses Loss of Plasticity in Continual Learning0
Experience Replay for Continual Learning0
Explainability and Continual Learning meet Federated Learning at the Network Edge0
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review0
Explainable Lifelong Stream Learning Based on "Glocal" Pairwise Fusion0
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts0
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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