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Found 7 papers, 5 papers with code
Reduction of Class Activation Uncertainty with Background Information
Through the class activation mappings (CAMs) of the trained models, we observed the tendency towards looking at a bigger picture with the proposed model training methodology.
Ranked #1 on
Satellite Image Classification
on STL-10, 40 Labels
Fine-Grained Image Classification
Satellite Image Classification
Uncertainty Aware Neural Network from Similarity and Sensitivity
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis
In this research, the PyTorch pre-trained models (VGG19\_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers.
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms
In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication(M2M), Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network.
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification
We also apply their usual fully-connected layer and the Spinal fully-connected layer to investigate the effectiveness of SpinalNet.
Ranked #1 on
Fine-Grained Image Classification
on Bird-225
(using extra training data)
SpinalNet: Deep Neural Network with Gradual Input
Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
Ranked #1 on
Satellite Image Classification
on STL-10, 40 Labels
Fine-Grained Image Classification
Satellite Image Classification
+1
Optimal Uncertainty-guided Neural Network Training
Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain.
