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 2176–2200 of 10307 papers

TitleStatusHype
Transfer Neyman-Pearson Algorithm for Outlier Detection—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
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning—0
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene—0
Mixture of Submodules for Domain Adaptive Person Search—0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration—0
ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White BalanceCode0
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition—0
DKC: Differentiated Knowledge Consolidation for Cloth-Hybrid Lifelong Person Re-identificationCode0
Subspace Constraint and Contribution Estimation for Heterogeneous Federated LearningCode0
A Unified Framework for Heterogeneous Semi-supervised Learning—0
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement—0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes—0
Navigating Nuance: In Quest for Political TruthCode0
Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning—0
Addressing Challenges in Data Quality and Model Generalization for Malaria Detection—0
Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction—0
Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning—0
Sample Correlation for Fingerprinting Deep Face RecognitionCode0
Class-based Subset Selection for Transfer Learning under Extreme Label Shift—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—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