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 5926–5950 of 10307 papers

TitleStatusHype
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation—0
HistoKT: Cross Knowledge Transfer in Computational PathologyCode0
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
Going Extreme: Comparative Analysis of Hate Speech in Parler and GabCode0
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency ParsingCode0
Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network—0
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study—0
A Survey on Visual Transfer Learning using Knowledge Graphs—0
Discovering Phonetic Inventories with Crosslingual Automatic Speech RecognitionCode0
Class-Aware Adversarial Transformers for Medical Image Segmentation—0
Gap Minimization for Knowledge Sharing and Transfer—0
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection—0
Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"—0
Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative ModelsCode0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer—0
VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models—0
Transfer Learning for Fault Diagnosis of Transmission Lines—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
Cross-Domain Few-Shot Graph ClassificationCode0
Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channelsCode0
A Review of Deep Transfer Learning and Recent Advancements—0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech—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