SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 5526–5550 of 10307 papers

TitleStatusHype
Meta Arcade: A Configurable Environment Suite for Meta-Learning—0
Meta Dialogue Policy Learning—0
Meta Distant Transfer Learning for Pre-trained Language Models—0
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration—0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Meta-free few-shot learning via representation learning with weight averaging—0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection—0
Meta-Learning for Few-Shot Land Cover Classification—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning—0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
Meta-Learning of Neural State-Space Models Using Data From Similar Systems—0
Meta-learning Transferable Representations with a Single Target Domain—0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization—0
MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling—0
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal—0
Meta Transfer Learning for Emotion Recognition—0
Meta Transfer Learning for Facial Emotion Recognition—0
MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks—0
Meta Variance Transfer: Learning to Augment from the Others—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified