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 6251–6275 of 10307 papers

TitleStatusHype
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images—0
Identification of pneumonia on chest x-ray images through machine learning—0
Identification of Social-Media Platform of Videos through the Use of Shared Features—0
Identifying disease-free chest X-ray images with deep transfer learning—0
Identifying Individual Dogs in Social Media Images—0
Identifying individual facial expressions by deconstructing a neural network—0
Identifying Intention Posts in Discussion Forums—0
Identifying Misinformation from Website Screenshots—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
Identifying regions of interest in whole slide images of renal cell carcinoma—0
Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions—0
Self-supervised Transformer for Deepfake Detection—0
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification—0
IDPG: An Instance-Dependent Prompt Generation Method—0
IDPG: An Instance-Dependent Prompt Generation Method—0
"I'd rather just go to bed": Understanding Indirect Answers—0
``I'd rather just go to bed'': Understanding Indirect Answers—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System—0
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT—0
IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Image Analytics for Legal Document Review: A Transfer Learning Approach—0
Image augmentation improves few-shot classification performance in plant disease recognition—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