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 3326–3350 of 10307 papers

TitleStatusHype
Automatic location detection based on deep learningCode0
LuoJiaHOG: A Hierarchy Oriented Geo-aware Image Caption Dataset for Remote Sensing Image-Text Retrival—0
TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model—0
On the low-shot transferability of [V]-Mamba—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Open Continual Feature Selection via Granular-Ball Knowledge TransferCode0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration—0
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Low-Energy On-Device Personalization for MCUsCode0
Conditional computation in neural networks: principles and research trends—0
Knowledge Transfer across Multiple Principal Component Analysis Studies—0
Discovering High-Strength Alloys via Physics-Transfer Learning—0
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
Cross-user activity recognition via temporal relation optimal transport—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal DocumentsCode0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Cross-domain and Cross-dimension Learning for Image-to-Graph TransformersCode0
Pre-Trained Model Recommendation for Downstream Fine-tuning—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