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 5626–5650 of 10307 papers

TitleStatusHype
Transfer Learning Based Efficient Traffic Prediction with Limited Training Data—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting—0
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding—0
Keratoconus Classifier for Smartphone-based Corneal Topographer—0
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning—0
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance—0
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
RCMNet: A deep learning model assists CAR-T therapy for leukemia—0
Large Scale Transfer Learning for Differentially Private Image Classification—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models—0
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?—0
Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer LearningCode0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression ExtractionCode0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification—0
Evaluating zero-shot transfers and multilingual models for dependency parsing and POS tagging within the low-resource language family Tupían—0
Polite Task-oriented Dialog Agents: To Generate or to Rewrite?—0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?—0
Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—0
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG—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