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
Deep learning for time series classificationCode2
Cross-lingual Contextualized Topic Models with Zero-shot LearningCode2
PubLayNet: largest dataset ever for document layout analysisCode2
Content-Based Search for Deep Generative ModelsCode2
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive SurveyCode2
CLIP-Driven Universal Model for Organ Segmentation and Tumor DetectionCode2
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
CARTE: Pretraining and Transfer for Tabular LearningCode2
CLAP: Learning Transferable Binary Code Representations with Natural Language SupervisionCode2
Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial ContextCode2
Continual Pre-training of Language ModelsCode2
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons ImagesCode2
AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scansCode2
Deep Model ReassemblyCode2
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A ReviewCode2
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact SimulationCode2
Discovery of 2D materials using Transformer Network based Generative DesignCode2
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud AnalysisCode2
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit DetectorsCode2
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical TasksCode2
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image SegmentationCode2
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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