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 576–600 of 10307 papers

TitleStatusHype
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer LearningCode1
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
Boosting Weakly Supervised Object Detection with Progressive Knowledge TransferCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional NetworkCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Bridging Anaphora Resolution as Question AnsweringCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
Amalgamating Knowledge From Heterogeneous Graph Neural NetworksCode1
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved TransferabilityCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Alice: Proactive Learning with Teacher's Demonstrations for Weak-to-Strong GeneralizationCode1
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World DomainsCode1
Amplifying Membership Exposure via Data PoisoningCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
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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