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 5901–5925 of 10307 papers

TitleStatusHype
Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy—0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models—0
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval—0
Text recognition on images using pre-trained CNN—0
Text-Speech Language Models with Improved Cross-Modal Transfer by Aligning Abstraction Levels—0
Text-to-Code Generation with Modality-relative Pre-training—0
Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning—0
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning—0
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents—0
The Actor-Advisor: Policy Gradient With Off-Policy Advice—0
The Amazing World of Neural Language Generation—0
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
Theater Aid System for the Visually Impaired Through Transfer Learning of Spatio-Temporal Graph Convolution Networks—0
The Bayesian Approach to Continual Learning: An Overview—0
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation—0
The Changing Role of Entrepreneurial Universities in the Altering Innovation Policy: Opportunities Arising from the Paradigm Change in Light of the Experience of Széchenyi István University—0
The Comparison of Individual Cat Recognition Using Neural Networks—0
The Complex Negotiation Dialogue Game—0
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks—0
The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff—0
The curious case of developmental BERTology: On sparsity, transfer learning, generalization and the brain—0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence—0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence—0
PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework—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