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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 3801–3825 of 10307 papers

TitleStatusHype
Evolution of transfer learning in natural language processing—0
Exploring the flavor structure of leptons via diffusion models—0
Evolution of ReID: From Early Methods to LLM Integration—0
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration—0
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning—0
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
A QUBO Framework for Team Formation—0
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks—0
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy—0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit—0
Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis—0
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation—0
Exploring transfer learning for Deep NLP systems on rarely annotated languages—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Everything is a Video: Unifying Modalities through Next-Frame Prediction—0
Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras—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