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 6301–6325 of 10307 papers

TitleStatusHype
Bayesian Optimization of Bilevel Problems—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
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning—0
Bayesian Transfer Learning—0
Rapid Classification of Glaucomatous Fundus Images—0
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning—0
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion—0
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules—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
RCMNet: A deep learning model assists CAR-T therapy for leukemia—0
Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions to Fearful and Shocking Events for Enhanced Sociability—0
READ: Recurrent Adaptation of Large Transformers—0
Realized Volatility Forecasting for New Issues and Spin-Offs using Multi-Source Transfer Learning—0
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation—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