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 7451–7475 of 10307 papers

TitleStatusHype
Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework—0
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates—0
Measuring Density and Similarity of Task Relevant Information in Neural Representations—0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—0
Measuring Information Transfer in Neural Networks—0
Measuring training variability from stochastic optimization using robust nonparametric testing—0
Measuring the Data Efficiency of Deep Learning Methods—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
An analytic theory of generalization dynamics and transfer learning in deep linear networks—0
Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks—0
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI—0
An analysis of the transfer learning of convolutional neural networks for artistic images—0
An Analysis of RF Transfer Learning Behavior Using Synthetic Data—0
An Analysis of Encoder Representations in Transformer-Based Machine Translation—0
The Reality of Multi-Lingual Machine Translation—0
An analysis of data variation and bias in image-based dermatological datasets for machine learning classification—0
The Actor-Advisor: Policy Gradient With Off-Policy Advice—0
Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things—0
SNN: Stacked Neural Networks—0
Medical Multimodal Classifiers Under Scarce Data Condition—0
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models—0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application—0
The Amazing World of Neural Language Generation—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