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 276–300 of 10307 papers

TitleStatusHype
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets—0
Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring—0
TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics DataCode2
Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML DatasetCode0
Transfer Learning for Temporal Link PredictionCode0
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge—0
UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person RetrievalCode0
The Impact of Model Zoo Size and Composition on Weight Space LearningCode0
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration—0
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction—0
Inferring genotype-phenotype maps using attention modelsCode0
Self-Controlled Dynamic Expansion Model for Continual Learning—0
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs—0
MultiLoKo: a multilingual local knowledge benchmark for LLMs spanning 31 languagesCode1
Query-based Knowledge Transfer for Heterogeneous Learning Environments—0
Towards On-Device Learning and Reconfigurable Hardware Implementation for Encoded Single-Photon Signal Processing—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Conditional Data Synthesis Augmentation—0
Detect Anything 3D in the WildCode3
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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