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 3026–3050 of 10307 papers

TitleStatusHype
Study of Vision Transformers for Covid-19 Detection from Chest X-rays—0
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems—0
Domain Adaptation using Silver Standard Masks for Lateral Ventricle Segmentation in FLAIR MRI—0
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case StudyCode0
Cumulative Spatial Knowledge Distillation for Vision TransformersCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language ModelsCode1
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality—0
SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology predictionCode0
SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos—0
A Topical Approach to Capturing Customer Insight In Social Media—0
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition—0
MGit: A Model Versioning and Management System—0
Improving BERT with Hybrid Pooling Network and Drop Mask—0
Improving Zero-Shot Generalization for CLIP with Synthesized PromptsCode1
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
AnyStar: Domain randomized universal star-convex 3D instance segmentationCode1
A decision framework for selecting information-transfer strategies in population-based SHM—0
Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing ImagesCode0
Agreement Tracking for Multi-Issue Negotiation Dialogues—0
Prototypical Contrastive Transfer Learning for Multimodal Language Understanding—0
Global birdsong embeddings enable superior transfer learning for bioacoustic classificationCode2
Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images—0
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions—0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—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