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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5501–5525 of 10307 papers

TitleStatusHype
Medical Multimodal Classifiers Under Scarce Data Condition—0
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application—0
MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis—0
MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks—0
Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation—0
Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets—0
MEG Decoding Across Subjects—0
MelNet: A Real-Time Deep Learning Algorithm for Object Detection—0
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models—0
Memory Efficient Class-Incremental Learning for Image Classification—0
Memory-efficient Continual Learning with Neural Collapse Contrastive—0
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model—0
Memory Oriented Transfer Learning for Semi-Supervised Image Deraining—0
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes—0
Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning—0
MENTOR: Human Perception-Guided Pretraining for Increased Generalization—0
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities—0
Merging Language and Domain Specific Models: The Impact on Technical Vocabulary Acquisition—0
MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn—0
Mesh-Wise Prediction of Demographic Composition from Satellite Images Using Multi-Head Convolutional Neural Network—0
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions—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