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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6601–6625 of 10307 papers

TitleStatusHype
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Investigating Transfer Learning in Graph Neural Networks—0
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets—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
A Pathology-Based Machine Learning Method to Assist in Epithelial Dysplasia Diagnosis—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
A PAC-Bayesian bound for Lifelong Learning—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
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings—0
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP—0
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability—0
Isotonic Data Augmentation for Knowledge Distillation—0
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering—0
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling—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
Show:102550
← PrevPage 265 of 413Next →
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