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 5126–5150 of 10307 papers

TitleStatusHype
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