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 4626–4650 of 10307 papers

TitleStatusHype
How to Adapt Your Large-Scale Vision-and-Language Model—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
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
How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching—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
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
Huawei’s Submissions to the WMT20 Biomedical Translation Task—0
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models—0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]—0
Human experts vs. machines in taxa recognition—0
Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation—0
Human Recognition Using Face in Computed Tomography—0
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios—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