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 2076–2100 of 10307 papers

TitleStatusHype
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition—0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
Action Learning for 3D Point Cloud Based Organ Segmentation—0
Actionable Phrase Detection using NLP—0
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario—0
Causal Reinforcement Learning: A Survey—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Causally Regularized Learning with Agnostic Data Selection Bias—0
Crowd-Powered Data Mining—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products—0
Transfer Learning for Individual Treatment Effect Estimation—0
Advantages of biologically-inspired adaptive neural activation in RNNs during learning—0
Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck—0
Causality in Neural Networks -- An Extended Abstract—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
CrossVoice: Crosslingual Prosody Preserving Cascade-S2ST using Transfer Learning—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—0
Causal Inference from Small High-dimensional Datasets—0
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction—0
A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium—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