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 1751–1775 of 10307 papers

TitleStatusHype
Multi-level datasets training method in Physics-Informed Neural Networks—0
OpenAVS: Training-Free Open-Vocabulary Audio Visual Segmentation with Foundational Models—0
Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing—0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks—0
Transfer Learning Under High-Dimensional Network Convolutional Regression Model—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning—0
Post-Transfer Learning Statistical Inference in High-Dimensional Regression—0
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation—0
Unifying Direct and Indirect Learning for Safe Control of Linear Systems—0
On the workflow, opportunities and challenges of developing foundation model in geophysics—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
Improving Local Air Quality Predictions Using Transfer Learning on Satellite Data and Graph Neural Networks—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Transfer Learning for High-dimensional Reduced Rank Time Series Models—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation—0
π_0.5: a Vision-Language-Action Model with Open-World Generalization—0
Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?—0
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence—0
PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVCode0
Histogram-based Parameter-efficient Tuning for Passive Sonar ClassificationCode0
Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—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