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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4626–4650 of 10307 papers

TitleStatusHype
Software Vulnerability Prediction Knowledge Transferring Between Programming Languages—0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
Overwriting Pretrained Bias with Finetuning DataCode0
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object DetectionCode0
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme—0
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks—0
Your representations are in the network: composable and parallel adaptation for large scale models—0
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study—0
Computing with Categories in Machine Learning—0
Environment Invariant Linear Least SquaresCode0
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning—0
A Comparison of Methods for Neural Network Aggregation—0
Cross-Lingual Transfer Learning for Alzheimer's Detection From Spontaneous Speech—0
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer LearningCode0
Training-Free Acceleration of ViTs with Delayed Spatial MergingCode0
Exploring Self-Supervised Representation Learning For Low-Resource Medical Image AnalysisCode0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes FrameworkCode0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Optimal transfer protocol by incremental layer defrosting—0
Artificial Intelligence for Dementia Research Methods Optimization—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction—0
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice ModelsCode0
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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