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 4751–4775 of 10307 papers

TitleStatusHype
On the Value of Target Data in Transfer Learning—0
On the workflow, opportunities and challenges of developing foundation model in geophysics—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
On Training Sketch Recognizers for New Domains—0
On Transferability of Prompt Tuning for Natural Language Processing—0
On Transfer in Classification: How Well do Subsets of Classes Generalize?—0
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver—0
On Hypothesis Transfer Learning of Functional Linear Models—0
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation—0
On Transfer Learning of Traditional Frequency and Time Domain Features in Turning—0
On transfer learning using a MAC model variant—0
On Using Transfer Learning For Plant Disease Detection—0
OpenAg: Democratizing Agricultural Intelligence—0
OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models—0
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine—0
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis—0
Open-Set Crowdsourcing using Multiple-Source Transfer Learning—0
Open Set Dandelion Network for IoT Intrusion Detection—0
Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator—0
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application—0
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions—0
Optimal Bayesian Transfer Learning—0
Optimal Layer Selection for Latent Data Augmentation—0
Optimal Policy Adaptation under Covariate Shift—0
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion—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