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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–925 of 10307 papers

TitleStatusHype
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based ApproachCode1
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
Pre-training technique to localize medical BERT and enhance biomedical BERTCode1
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose trackingCode1
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANsCode1
Deep Image Harmonization by Bridging the Reality GapCode1
Merging Models with Fisher-Weighted AveragingCode1
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta LearningCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Deep Learning Based Assessment of Synthetic Speech NaturalnessCode1
Meta-Transfer Learning for Code-Switched Speech RecognitionCode1
Deep Learning Approach to Diabetic Retinopathy DetectionCode1
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS datasetCode1
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and TrackingCode1
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
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout AnalysisCode1
AquaVision: Automating the detection of waste in water bodies using deep transfer learningCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Deep Learning Enabled Semantic Communication SystemsCode1
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out StrategiesCode1
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLCode1
Do Adversarially Robust ImageNet Models Transfer Better?Code1
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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