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 9026–9050 of 10307 papers

TitleStatusHype
Causality in Neural Networks -- An Extended Abstract—0
QiaoNing at SemEval-2020 Task 4: Commonsense Validation and Explanation system based on ensemble of language model—0
Transfer Learning for Individual Treatment Effect Estimation—0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
Causally Regularized Learning with Agnostic Data Selection Bias—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Causal Reinforcement Learning: A Survey—0
Q-MRS: A Deep Learning Framework for Quantitative Magnetic Resonance Spectra Analysis—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
Breast mass detection in digital mammography based on anchor-free architecture—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images—0
Adaptive Transfer Learning: a simple but effective transfer learning—0
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models—0
CDS: Cross-Domain Self-Supervised Pre-Training—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
Quality Diversity for Visual Pre-Training—0
Quality Estimation Using Dual Encoders with Transfer Learning—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—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