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 3601–3625 of 10307 papers

TitleStatusHype
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0—0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Bayesian Transfer Learning—0
Deep transfer learning for visual analysis and attribution of paintings by RaphaelCode0
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate DatasetsCode0
Value Explicit Pretraining for Learning Transferable Representations—0
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif DiscoveryCode0
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer—0
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas—0
Social Learning: Towards Collaborative Learning with Large Language Models—0
LaViP:Language-Grounded Visual Prompts—0
Domain adaption and physical constrains transfer learning for shale gas production—0
Federated Multi-View Synthesizing for Metaverse—0
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System—0
Semantic Segmentation Using Transfer Learning on Fisheye Images—0
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation—0
p-Laplacian Adaptation for Generative Pre-trained Vision-Language ModelsCode0
Cross-Domain Robustness of Transformer-based Keyphrase Generation—0
Optimizing Dense Feed-Forward Neural Networks—0
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian TextsCode0
One Self-Configurable Model to Solve Many Abstract Visual Reasoning ProblemsCode0
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning—0
Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study—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