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 126–150 of 10307 papers

TitleStatusHype
A Survey on Time-Series Pre-Trained ModelsCode2
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons ImagesCode2
Cross-lingual Contextualized Topic Models with Zero-shot LearningCode2
Densely Connected Parameter-Efficient Tuning for Referring Image SegmentationCode2
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
CLAP: Learning Transferable Binary Code Representations with Natural Language SupervisionCode2
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical TasksCode2
AdapterFusion: Non-Destructive Task Composition for Transfer LearningCode2
CARTE: Pretraining and Transfer for Tabular LearningCode2
PubLayNet: largest dataset ever for document layout analysisCode2
CLIP-Driven Universal Model for Organ Segmentation and Tumor DetectionCode2
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image SegmentationCode2
Content-Based Search for Deep Generative ModelsCode2
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact SimulationCode2
AddressCLIP: Empowering Vision-Language Models for City-wide Image Address LocalizationCode2
Deep learning for time series classificationCode2
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit DetectorsCode2
AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scansCode2
Any-point Trajectory Modeling for Policy LearningCode2
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud AnalysisCode2
Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial ContextCode2
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge TransferCode2
CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive SurveyCode2
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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