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 3476–3500 of 10307 papers

TitleStatusHype
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Confidence Preserving Machine for Facial Action Unit Detection—0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Confidence Aware Neural Networks for Skin Cancer Detection—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
A General Regularization Framework for Domain Adaptation—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
A Checkpoint on Multilingual Misogyny Identification—0
Conditional Neural Processes for Molecules—0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
A Smartphone-Based Skin Disease Classification Using MobileNet CNN—0
Conditional Data Synthesis Augmentation—0
Conditional computation in neural networks: principles and research trends—0
Conditional Bures Metric for Domain Adaptation—0
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference—0
A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Concurrent Discrimination and Alignment for Self-Supervised Feature 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