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 3851–3875 of 10307 papers

TitleStatusHype
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables—0
A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source—0
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
Event Camera Data Pre-training—0
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries—0
Face Mask Detection using Transfer Learning of InceptionV3—0
FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer—0
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain—0
Facial Action Unit Recognition Based on Transfer Learning—0
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition—0
Event Camera Data Dense Pre-training—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars—0
Evaluation-oriented Knowledge Distillation for Deep Face Recognition—0
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—0
Evaluation of Transfer Learning for Polish with a text-to-text model—0
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction—0
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography—0
CLIP-aware Domain-Adaptive Super-Resolution—0
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
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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