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 701–725 of 10307 papers

TitleStatusHype
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition—0
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets—0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
Scalable Forward-Forward Algorithm—0
Hybrid deep convolution model for lung cancer detection with transfer learning—0
Offline-to-online hyperparameter transfer for stochastic bandits—0
Representation Convergence: Mutual Distillation is Secretly a Form of RegularizationCode0
Transfer learning via Regularized Linear Discriminant Analysis—0
Learning Evolution via Optimization Knowledge Adaptation—0
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation—0
Improving Transducer-Based Spoken Language Understanding with Self-Conditioned CTC and Knowledge Transfer—0
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model—0
Transfer Neyman-Pearson Algorithm for Outlier Detection—0
Transfer Learning Analysis of Variational Quantum Circuits—0
Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition—0
BatStyler: Advancing Multi-category Style Generation for Source-free Domain GeneralizationCode0
Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning—0
ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White BalanceCode0
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene—0
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement—0
DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identificationCode0
Subspace Constraint and Contribution Estimation for Heterogeneous Federated LearningCode0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
A Unified Framework for Heterogeneous Semi-supervised Learning—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