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 551–575 of 10307 papers

TitleStatusHype
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—0
Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation—0
Protecting Intellectual Property of EEG-based Neural Networks with WatermarkingCode0
Topological derivative approach for deep neural network architecture adaptation—0
Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation—0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
Performance Evaluation of Image Enhancement Techniques on Transfer Learning for Touchless Fingerprint Recognition—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human ActivityCode0
How does a Multilingual LM Handle Multiple Languages?—0
A Theoretical Framework for Data Efficient Multi-Source Transfer Learning Based on Cramér-Rao Bound—0
Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure—0
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning—0
ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels—0
SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning—0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks—0
Prediction of the Most Fire-Sensitive Point in Building Structures with Differentiable Agents for Thermal Simulators—0
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies—0
TopoCL: Topological Contrastive Learning for Time Series—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
LLM-USO: Large Language Model-based Universal Sizing Optimizer—0
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation—0
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning StudyCode0
Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case StudyCode0
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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