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 226–250 of 10307 papers

TitleStatusHype
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional FiltersCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
BlackVIP: Black-Box Visual Prompting for Robust Transfer LearningCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Amplifying Membership Exposure via Data PoisoningCode1
A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect DetectionCode1
1st Place Solution to Google Landmark Retrieval 2020Code1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
An Improved Person Re-identification Method by light-weight convolutional neural networkCode1
Chip Placement with Deep Reinforcement LearningCode1
Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray ImagesCode1
Analysis of skin lesion images with deep learningCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
Bridge Correlational Neural Networks for Multilingual Multimodal Representation LearningCode1
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learningCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networksCode1
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?Code1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
Amalgamating Knowledge From Heterogeneous Graph Neural NetworksCode1
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
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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