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 4926–4950 of 10307 papers

TitleStatusHype
Investigating the role of model-based learning in exploration and transfer—0
Investigating Transfer Learning in Graph Neural Networks—0
Investigation of Transfer Languages for Parsing Latin: Italic Branch vs. Hellenic Branch—0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
In Your Pace: Learning the Right Example at the Right Time—0
iPINNs: Incremental learning for Physics-informed neural networks—0
Impact of Financial Literacy on Investment Decisions and Stock Market Participation using Extreme Learning Machines—0
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction—0
Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective—0
Is Discriminator a Good Feature Extractor?—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?—0
Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?—0
Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition—0
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP—0
Isotonic Data Augmentation for Knowledge Distillation—0
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering—0
Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization—0
Is Transfer Learning Necessary for Protein Landscape Prediction?—0
ISTRBoost: Importance Sampling Transfer Regression using Boosting—0
AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages—0
Iterative Auto-Annotation for Scientific Named Entity Recognition Using BERT-Based Models—0
Iterative Dual Domain Adaptation for Neural Machine Translation—0
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays—0
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images—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