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 5101–5125 of 10307 papers

TitleStatusHype
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems—0
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
Language Model Supervision for Handwriting Recognition Model Adaptation—0
Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?—0
Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions—0
Language Transfer Learning for Supervised Lexical Substitution—0
Language verY Rare for All—0
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction—0
Large Dimensional Analysis and Improvement of Multi Task Learning—0
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study—0
Large Language Models for Cyber Security: A Systematic Literature Review—0
Large Language Models for Market Research: A Data-augmentation Approach—0
Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning—0
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding—0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Large-Scale Generative Data-Free Distillation—0
Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer—0
Large Scale Transfer Learning for Differentially Private Image Classification—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts—0
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining—0
Latent Alignment with Deep Set EEG Decoders—0
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach—0
Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples—0
Latent-Insensitive autoencoders for Anomaly Detection—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