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 6151–6175 of 10307 papers

TitleStatusHype
Temporal Probabilistic Asymmetric Multi-task Learning—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox—0
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?—0
How to Parse a Creole: When Martinican Creole Meets French—0
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models—0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
How transferable are features in convolutional neural network acoustic models across languages?—0
How Transferable are Neural Networks in NLP Applications?—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
How Transferable are the Representations Learned by Deep Q Agents?—0
How transfer learning impacts linguistic knowledge in deep NLP models?—0
Self-Supervised Pre-Training for Precipitation Post-Processor—0
How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
The RWTH Aachen University Machine Translation Systems for WMT 2019—0
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images—0
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization—0
A review of sentiment analysis research in Arabic language—0
A Review of Deep Transfer Learning and Recent Advancements—0
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks—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