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 301–325 of 10307 papers

TitleStatusHype
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis—0
Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs—0
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network—0
Learning Optimal Prompt Ensemble for Multi-source Visual Prompt Transfer—0
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
Identifying regions of interest in whole slide images of renal cell carcinoma—0
Alice: Proactive Learning with Teacher's Demonstrations for Weak-to-Strong GeneralizationCode1
Hyperbolic Category Discovery—0
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents—0
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation—0
Cross-functional transferability in universal machine learning interatomic potentials—0
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method—0
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial ImageryCode0
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms—0
Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography-Based Motion Prediction Using Sliding-Window Normalization—0
Optimizing Specific and Shared Parameters for Efficient Parameter Tuning—0
Block Toeplitz Sparse Precision Matrix Estimation for Large-Scale Interval-Valued Time Series Forecasting—0
MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving PerceptionCode1
Scaling Analysis of Interleaved Speech-Text Language ModelsCode3
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction TuningCode1
Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks—0
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation—0
CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise—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