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 626–650 of 10307 papers

TitleStatusHype
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge TransferCode1
Amplifying Membership Exposure via Data PoisoningCode1
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out StrategiesCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Algorithmic encoding of protected characteristics in image-based models for disease detectionCode1
Audio-based Near-Duplicate Video Retrieval with Audio Similarity LearningCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19Code1
Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer LearningCode1
Audio Embeddings as Teachers for Music ClassificationCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual GroundingCode1
A unified framework for dataset shift diagnosticsCode1
A Unified Framework for Domain Adaptive Pose EstimationCode1
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive LearningCode1
AutoKE: An automatic knowledge embedding framework for scientific machine learningCode1
Authorship Style Transfer with Policy OptimizationCode1
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Pre-training technique to localize medical BERT and enhance biomedical BERTCode1
DocXClassifier: High Performance Explainable Deep Network for Document Image ClassificationCode1
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS datasetCode1
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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