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 4401–4425 of 10307 papers

TitleStatusHype
Osteosarcoma Tumor Detection using Transfer Learning Models—0
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion—0
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks—0
Predictive Models from Quantum Computer Benchmarks—0
Meta-models for transfer learning in source localisation—0
Towards Automated COVID-19 Presence and Severity Classification—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning—0
Cross-Language Transfer Learning using Visual Information for Automatic Sign Gesture Recognition—0
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN TrainingCode0
Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN—0
Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue GenerationCode0
Learning representations that are closed-form Monge mapping optimal with application to domain adaptationCode0
ML-Based Teaching Systems: A Conceptual Framework—0
To transfer or not transfer: Unified transferability metric and analysis—0
A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images—0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classificationCode0
Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2—0
Detection of depression on social networks using transformers and ensemblesCode0
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—0
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval—0
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
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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