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 951–975 of 10307 papers

TitleStatusHype
IndicBART: A Pre-trained Model for Indic Natural Language GenerationCode1
Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease ClassificationCode1
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionCode1
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
Robust fine-tuning of zero-shot modelsCode1
roadscene2vec: A Tool for Extracting and Embedding Road Scene-GraphsCode1
AraT5: Text-to-Text Transformers for Arabic Language GenerationCode1
Task-Oriented Dialogue System as Natural Language GenerationCode1
Knowledge Base Completion Meets Transfer LearningCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Frozen Pretrained Transformers for Neural Sign Language TranslationCode1
How Hateful are Movies? A Study and Prediction on Movie SubtitlesCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Do Vision Transformers See Like Convolutional Neural Networks?Code1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
KITTI-CARLA: a KITTI-like dataset generated by CARLA SimulatorCode1
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic SegmentationCode1
On the Opportunities and Risks of Foundation ModelsCode1
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language ModelsCode1
TVT: Transferable Vision Transformer for Unsupervised Domain AdaptationCode1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image AnalysisCode1
Semantic Concentration for Domain AdaptationCode1
Towards to Robust and Generalized Medical Image Segmentation FrameworkCode1
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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