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 4976–5000 of 10307 papers

TitleStatusHype
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition—0
AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain AdaptationCode0
Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning—0
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning—0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—0
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social MediaCode0
Using Human Perception to Regularize Transfer Learning—0
Hypothesis Transfer in Bandits by Weighted Models—0
Meta-Learning of Neural State-Space Models Using Data From Similar Systems—0
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction—0
Findings of the Covid-19 MLIA Machine Translation Task—0
Seeded iterative clustering for histology region identificationCode0
Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy—0
EVA: Exploring the Limits of Masked Visual Representation Learning at ScaleCode0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things—0
QueryForm: A Simple Zero-shot Form Entity Query Framework—0
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale—0
Sign Language to Text Conversion in Real Time using Transfer Learning—0
Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks—0
Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies—0
LLEDA -- Lifelong Self-Supervised Domain Adaptation—0
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular DataCode0
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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