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 8276–8300 of 10307 papers

TitleStatusHype
Unbiased Scene Graph Generation using Predicate Similarities—0
Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation—0
Uncertainty-aware Incremental Learning for Multi-organ Segmentation—0
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation—0
Uncertainty-Aware Multi-Expert Knowledge Distillation for Imbalanced Disease Grading—0
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning—0
Uncertainty in Multitask Transfer Learning—0
Uncertainty Regularized Multi-Task Learning—0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning—0
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction—0
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes—0
Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray—0
Understanding and Improving Information Transfer in Multi-Task Learning—0
Understanding and Leveraging the Learning Phases of Neural Networks—0
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Understanding Cross-Lingual Inconsistency in Large Language Models—0
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models—0
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis—0
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning—0
Understanding Social Networks using Transfer Learning—0
Understanding the Benefits of Image Augmentations—0
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models—0
Understanding the Mechanisms of Deep Transfer Learning for Medical Images—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