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 26–50 of 10307 papers

TitleStatusHype
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
BayLing 2: A Multilingual Large Language Model with Efficient Language AlignmentCode3
The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic SpeechCode3
The Role of Generative Systems in Historical Photography Management: A Case Study on Catalan ArchivesCode3
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge DistillationCode3
ECG-FM: An Open Electrocardiogram Foundation ModelCode3
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic BenchmarkingCode3
LLM4CP: Adapting Large Language Models for Channel PredictionCode3
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language ModelsCode3
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacousticsCode3
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time seriesCode3
cmaes : A Simple yet Practical Python Library for CMA-ESCode3
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer LearningCode3
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot LearningCode3
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational AbilitiesCode3
Relational Multi-Task Learning: Modeling Relations between Data and TasksCode3
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox OptimizationCode3
Robust and Efficient Medical Imaging with Self-SupervisionCode3
Pastiche Master: Exemplar-Based High-Resolution Portrait Style TransferCode3
ST-MoE: Designing Stable and Transferable Sparse Expert ModelsCode3
ResNeSt: Split-Attention NetworksCode3
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
Universal Language Model Fine-tuning for Text ClassificationCode3
Do MIL Models Transfer?Code2
MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language ModelsCode2
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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