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 2776–2800 of 10307 papers

TitleStatusHype
Crack-Net: Prediction of Crack Propagation in Composites—0
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition—0
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning—0
Attention Is All You Need For Blind Room Volume Estimation—0
Robust Navigation with Cross-Modal Fusion and Knowledge TransferCode0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Multiply Robust Federated Estimation of Targeted Average Treatment Effects—0
Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models—0
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts—0
Identification of pneumonia on chest x-ray images through machine learning—0
Brain Tumor Detection Using Deep Learning Approaches—0
Hand Gesture Recognition with Two Stage Approach Using Transfer Learning and Deep Ensemble Learning—0
Using Artificial Intelligence for the Automation of Knitting Patterns—0
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild—0
GECTurk: Grammatical Error Correction and Detection Dataset for TurkishCode1
Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal DistillationCode0
Long-tail Augmented Graph Contrastive Learning for RecommendationCode1
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
NoisyNN: Exploring the Impact of Information Entropy Change in Learning SystemsCode1
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
Semi-supervised Domain Adaptation in Graph Transfer Learning—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