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 4376–4400 of 10307 papers

TitleStatusHype
Causal Reinforcement Learning: A Survey—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction—0
Causally Regularized Learning with Agnostic Data Selection Bias—0
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Transfer Learning for Individual Treatment Effect Estimation—0
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products—0
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space—0
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation—0
Efficient Task Transfer for HLS DSE—0
Causality in Neural Networks -- An Extended Abstract—0
Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—0
Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space—0
Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning—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
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—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