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 1426–1450 of 10307 papers

TitleStatusHype
Does Pretraining for Summarization Require Knowledge Transfer?Code1
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer LearningCode1
Pre-trained Models for Sonar ImagesCode1
Pre-training and Diagnosing Knowledge Base Completion ModelsCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
Domain Adaptation of Thai Word Segmentation Models using Stacked EnsembleCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer LearningCode1
Principal Feature Visualisation in Convolutional Neural NetworksCode1
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?Code1
Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge TransferCode1
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation ModelsCode1
Prompt-enhanced Federated Content Representation Learning for Cross-domain RecommendationCode1
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic SegmentationCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Pruner: A Speculative Exploration Mechanism to Accelerate Tensor Program TuningCode1
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center StudyCode1
PTGB: Pre-Train Graph Neural Networks for Brain Network AnalysisCode1
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident AnalysisCode1
WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management TasksCode1
Do Vision Transformers See Like Convolutional Neural Networks?Code1
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image SegmentationCode1
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional FiltersCode1
Grounding Psychological Shape Space in Convolutional Neural NetworksCode1
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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