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

Showing 23262350 of 10307 papers

TitleStatusHype
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-ScansCode0
GAN Cocktail: mixing GANs without dataset accessCode0
Analysis and Prediction of NLP Models Via Task EmbeddingsCode0
Functional Knowledge Transfer with Self-supervised Representation LearningCode0
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural NetworksCode0
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformationsCode0
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
From Video Game to Real Robot: The Transfer between Action SpacesCode0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text ClassificationCode0
From Colors to Classes: Emergence of Concepts in Vision TransformersCode0
Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity RecognitionCode0
From English to Code-Switching: Transfer Learning with Strong Morphological CluesCode0
An AI-Powered VVPAT Counter for Elections in IndiaCode0
Automatic Online Multi-Source Domain AdaptationCode0
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal ImagesCode0
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide SurveyCode0
Free the Plural: Unrestricted Split-Antecedent Anaphora ResolutionCode0
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning TechniquesCode0
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across TokenizersCode0
Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text ClassificationCode0
Automatic location detection based on deep learningCode0
FOSI: Hybrid First and Second Order OptimizationCode0
AdaRank: Disagreement Based Module Rank Prediction for Low-rank AdaptationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified