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 5051–5075 of 10307 papers

TitleStatusHype
Joint Semantic Transfer Network for IoT Intrusion Detection—0
Multi-Target Tracking with Transferable Convolutional Neural NetworksCode0
Drug repositioning for Alzheimer's disease with transfer learning—0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning—0
Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured LearningCode0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
Visual Answer Localization with Cross-modal Mutual Knowledge TransferCode0
Trade-off between reconstruction loss and feature alignment for domain generalizationCode0
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence—0
This joke is [MASK]: Recognizing Humor and Offense with Prompting—0
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic ParsingCode0
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
Progressive Sentiment Analysis for Code-Switched Text DataCode0
Accelerating Certified Robustness Training via Knowledge TransferCode0
Embedding neurophysiological signalsCode0
Langevin dynamics based algorithm e-THO POULA for stochastic optimization problems with discontinuous stochastic gradientCode0
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection ModelsCode0
IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation—0
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning—0
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla—0
Knowledge Transfer from Answer Ranking to Answer Generation—0
Data Augmentation for Automated Essay Scoring using Transformer Models—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