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 5101–5150 of 10307 papers

TitleStatusHype
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Public Parking Spot Detection And Geo-localization Using Transfer Learning—0
PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories—0
Pushing the Limits of AMR Parsing with Self-Learning—0
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization—0
QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model—0
Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis—0
Quality Diversity for Visual Pre-Training—0
Quality Estimation Using Dual Encoders with Transfer Learning—0
Quality In, Quality Out: Learning from Actual Mistakes—0
Quality versus Quantity: Building Catalan-English MT Resources—0
Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting—0
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
Quantifying the Performance of Federated Transfer Learning—0
Quantifying the value of information transfer in population-based SHM—0
Quantifying the value of positive transfer: An experimental case study—0
Quantum Federated Learning With Quantum Networks—0
Quantum median filter for Total Variation image denoising—0
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes—0
Quantum Transfer Learning for Acceptability Judgements—0
Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach—0
Quantum Transfer Learning for Wi-Fi Sensing—0
CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model—0
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics—0
Query-based Knowledge Transfer for Heterogeneous Learning Environments—0
QueryForm: A Simple Zero-shot Form Entity Query Framework—0
Question answering using deep learning in low resource Indian language Marathi—0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
RailSem19: A Dataset for Semantic Rail Scene Understanding—0
An Improved Transfer Model: Randomized Transferable Machine—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification—0
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index—0
Ranking Neural Checkpoints—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning—0
Rapid Classification of Glaucomatous Fundus Images—0
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion—0
Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning—0
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications—0
Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"—0
Razmecheno: Named Entity Recognition from Digital Archive of Diaries “Prozhito”—0
r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches—0
RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis—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