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 7701–7725 of 10307 papers

TitleStatusHype
Amobee at IEST 2018: Transfer Learning from Language Models—0
Modularity in biological evolution and evolutionary computation—0
Modularized data-driven approximation of the Koopman operator and generator—0
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning—0
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning—0
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models—0
Modular network for high accuracy object detection—0
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection—0
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting—0
MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets—0
MoFE: Mixture of Frozen Experts Architecture—0
MoKD: Multi-Task Optimization for Knowledge Distillation—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
A Minimax Game for Instance based Selective Transfer Learning—0
Molecular Subtype Prediction for Breast Cancer Using H&E Specialized Backbone—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing—0
MOMA:Distill from Self-Supervised Teachers—0
MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Rendering—0
Monaural speech enhancement on drone via Adapter based transfer learning—0
Monitoring crop phenology with street-level imagery using computer vision—0
SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Color Editing—0
Monkey Transfer Learning Can Improve Human Pose Estimation—0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision—0
Monolingual and Cross-Lingual Knowledge Transfer for Topic Classification—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