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 1976–2000 of 10307 papers

TitleStatusHype
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling—0
Iterative Auto-Annotation for Scientific Named Entity Recognition Using BERT-Based Models—0
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report SummarizationCode0
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba—0
Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas—0
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality—0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
Multimodal Quantitative Language for Generative Recommendation—0
Distribution Matching for Self-Supervised Transfer LearningCode0
Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes—0
A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection—0
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity—0
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs—0
Asymmetric Co-Training for Source-Free Few-Shot Domain AdaptationCode0
Appeal prediction for AI up-scaled ImagesCode0
Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical PerspectiveCode0
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements—0
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code GenerationCode0
Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms—0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models—0
Transfer Learning of CATE with Kernel Ridge RegressionCode0
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models—0
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency—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