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 4476–4500 of 10307 papers

TitleStatusHype
Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation—0
UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis—0
Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations—0
Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations—0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation—0
Learning to Plan with Natural LanguageCode0
Analyzing the Domain Shift Immunity of Deep Homography EstimationCode0
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite ImageryCode0
Knowledge Distillation Under Ideal Joint Classifier Assumption—0
Independent Feature Decomposition and Instance Alignment for Unsupervised Domain AdaptationCode0
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review—0
UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation—0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection—0
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention—0
MisRoBÆRTa: Transformers versus MisinformationCode0
PBNR: Prompt-based News Recommender System—0
Neural Machine Translation For Low Resource LanguagesCode0
Generating an interactive online map of future sea level rise along the North Shore of Vancouver: methods and insights on enabling geovisualisation for coastal communities—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator—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