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 8651–8675 of 10307 papers

TitleStatusHype
These are Not All the Features You are Looking For: A Fundamental Bottleneck In Supervised PretrainingCode0
Towards an efficient deep learning model for musical onset detectionCode0
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classificationCode0
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence MatchingCode0
Transferring Robustness for Graph Neural Network Against Poisoning AttacksCode0
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer LearningCode0
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual LearningCode0
IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim DetectionCode0
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic FeaturesCode0
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate DiagnosisCode0
Unsupervised Representation Learning by Balanced Self Attention MatchingCode0
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric EstimationCode0
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem SolvingCode0
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response PredictionCode0
Privacy-Aware Lifelong LearningCode0
Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning ApproachCode0
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small dataCode0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane DetectionCode0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White BalanceCode0
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social MediaCode0
A Brief Review of Hypernetworks in Deep LearningCode0
Absolute Zero-Shot LearningCode0
Accelerating Certified Robustness Training via Knowledge TransferCode0
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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