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 9426–9450 of 10307 papers

TitleStatusHype
Continual Prompt Tuning for Dialog State Tracking—0
Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models—0
Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning—0
Real-Time Mask Detection Based on SSD-MobileNetV2—0
Bibliometric-enhanced Information Retrieval: 2nd International BIR Workshop—0
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao—0
Real-time Plant Health Assessment Via Implementing Cloud-based Scalable Transfer Learning On AWS DeepLens—0
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay—0
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor—0
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Continuous Transfer Learning—0
Continuous Transfer Learning for UAV Communication-aware Trajectory Design—0
Continuous Transfer Learning with Label-informed Distribution Alignment—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Contradiction Detection in Persian Text—0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency—0
Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol—0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning—0
Real-time Sign Language Recognition Using MobileNetV2 and Transfer Learning—0
Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis—0
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for 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