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 7101–7125 of 10307 papers

TitleStatusHype
Learning unbiased features—0
Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique—0
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification—0
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation—0
Learning Universal Policies via Text-Guided Video Generation—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play—0
Learning Visually Consistent Label Embeddings for Zero-Shot Learning—0
An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection—0
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models—0
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding—0
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Learn it or Leave it: Module Composition and Pruning for Continual Learning—0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Learn to Talk via Proactive Knowledge Transfer—0
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics—0
LEEP: A New Measure to Evaluate Transferability of Learned Representations—0
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs—0
LegalTurk Optimized BERT for Multi-Label Text Classification and NER—0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery—0
LEKA:LLM-Enhanced Knowledge Augmentation—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—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