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 6276–6300 of 10307 papers

TitleStatusHype
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—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
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users—0
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images—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
Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother—0
QueryForm: A Simple Zero-shot Form Entity Query Framework—0
Bayesian Model Adaptation for Crowd Counts—0
Question answering using deep learning in low resource Indian language Marathi—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—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
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors—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