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 6126–6150 of 10307 papers

TitleStatusHype
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Transferability Metrics for Selecting Source Model Ensembles—0
Transferability Estimation using Bhattacharyya Class Separability—0
Improving Customer Service Chatbots with Attention-based Transfer Learning—0
Semi-Online Knowledge DistillationCode0
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning—0
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review—0
Multi-label Iterated Learning for Image Classification with Label AmbiguityCode0
KTNet: Knowledge Transfer for Unpaired 3D Shape CompletionCode0
CL-NERIL: A Cross-Lingual Model for NER in Indian LanguagesCode0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart HomesCode0
Transfer Learning with Gaussian Processes for Bayesian OptimizationCode0
Adaptive Transfer Learning: a simple but effective transfer learning—0
Component Transfer Learning for Deep RL Based on Abstract RepresentationsCode0
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality DetectionCode0
Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis—0
The Joy of Neural Painting—0
An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood VesselsCode0
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints—0
Combined Scaling for Zero-shot Transfer Learning—0
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning—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