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 8801–8825 of 10307 papers

TitleStatusHype
Arabic Dialect Identification Using BERT Fine-TuningCode0
Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiogramsCode0
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph DecoderCode0
A Resource-Efficient Training Framework for Remote Sensing Text--Image RetrievalCode0
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual GeneralizationCode0
A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 PandemicCode0
Are we done with object recognition? The iCub robot's perspectiveCode0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance LabelingCode0
ArmanEmo: A Persian Dataset for Text-based Emotion DetectionCode0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural networkCode0
A Sample-Level Evaluation and Generative Framework for Model Inversion AttacksCode0
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Code0
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life PredictionCode0
A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave ProcessingCode0
A shared neural encoding model for the prediction of subject-specific fMRI responseCode0
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer LearningCode0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
Asking and Answering Questions to Extract Event-Argument StructuresCode0
Asking Crowdworkers to Write Entailment Examples: The Best of Bad OptionsCode0
Aspect-augmented Adversarial Networks for Domain AdaptationCode0
A Split-then-Join Approach to Abstractive Summarization for Very Long Documents in a Low Resource SettingCode0
Assaying Out-Of-Distribution Generalization in Transfer LearningCode0
Show:102550
← PrevPage 353 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