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 5701–5725 of 10307 papers

TitleStatusHype
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach—0
From N to N+1: Multiclass Transfer Incremental Learning—0
UNER: A Unified Prediction Head for Named Entity Recognition in Visually-rich Documents—0
A Brief Survey of Multilingual Neural Machine Translation—0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Frustratingly Easy Natural Question Answering—0
Frustratingly Easy Transferability Estimation—0
Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations—0
A Survey of Latent Factor Models in Recommender Systems—0
FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task—0
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities—0
FTL: A universal framework for training low-bit DNNs via Feature Transfer—0
SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild—0
SEALion: a Framework for Neural Network Inference on Encrypted Data—0
Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns—0
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies—0
Fully Automated Hand Hygiene Monitoring\ Operating Room using 3D Convolutional Neural Network—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier—0
Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation—0
Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning—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