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 801–825 of 10307 papers

TitleStatusHype
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident AnalysisCode1
Graphonomy: Universal Human Parsing via Graph Transfer LearningCode1
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image SegmentationCode1
AraT5: Text-to-Text Transformers for Arabic Language GenerationCode1
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual LearningCode1
Head2Toe: Utilizing Intermediate Representations for Better Transfer LearningCode1
AKHCRNet: Bengali Handwritten Character Recognition Using Deep LearningCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation LearningCode1
Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networksCode1
High-throughput molecular imaging via deep learning enabled Raman spectroscopyCode1
HiViT: Hierarchical Vision Transformer Meets Masked Image ModelingCode1
HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language ModelsCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve BackbonesCode1
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot LearningCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
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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