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 4301–4325 of 10307 papers

TitleStatusHype
Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability—0
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP—0
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection—0
Augmenting Character Designers Creativity Using Generative Adversarial Networks—0
3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors—0
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning—0
Heterogeneous Federated Learning via Personalized Generative Networks—0
Heterogeneous Transfer Learning in Ensemble Clustering—0
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models—0
Curricular Transfer Learning for Sentence Encoded Tasks—0
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification—0
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20—0
CUNI Submission for the Inuktitut Language in WMT News 2020—0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
CUNI Submission for Low-Resource Languages in WMT News 2019—0
CUNI Basque-to-English Submission in IWSLT18—0
AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation—0
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks—0
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Cued Speech Generation Leveraging a Pre-trained Audiovisual Text-to-Speech Model—0
Audiovisual transfer learning for audio tagging and sound event detection—0
Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy—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