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 5576–5600 of 10307 papers

TitleStatusHype
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning—0
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions—0
Minimum-Norm Interpolation Under Covariate Shift—0
Min-Max Statistical Alignment for Transfer Learning—0
Minority Class Oriented Active Learning for Imbalanced Datasets—0
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks—0
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations—0
Missing Features Reconstruction and Its Impact on Classification Accuracy—0
MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection—0
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework—0
Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis—0
Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography-Based Motion Prediction Using Sliding-Window Normalization—0
MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection—0
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps—0
Mixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations—0
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models—0
Mixture of Latent Experts Using Tensor Products—0
Mixture of Submodules for Domain Adaptive Person Search—0
ML-Based Teaching Systems: A Conceptual Framework—0
MLDGG: Meta-Learning for Domain Generalization on Graphs—0
MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion—0
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding—0
MOBA: Multi-teacher Model Based Reinforcement Learning—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