To evaluate the trained model for Open Set Classification Rate (OSCR) and Out-of-Distribution (OOD) detection setting, add--evalafter the training command. 3. Results We visualize the deep feature of Softmax/GCPL/ARPL/ARPL+CS as below. ...
因此,我们可以通过联合利用两级决策中的不确定性和动作标注来解决上面的第一个挑战(见表1),通过PU学习来解决第二个挑战(第3.2节) 3.1. Action Classification K-way Uncertainty-aware Classification 根据现有的证据深度学习(EDL)[4,53],它可以有效地量化分类的不确定性,我们假设分类概率p∈RK上的狄利克雷分布Dir...
-r to resume training from a checkpoint with a lower learning rate. -t to plot the training curves on tensorboardX --name n uses n in the name for tensorboard and saving the weights. By default it is "myTest".EvaluationThe evaluation scripts calculate the classification accuracy on the ...
To fill this gap, this paper investigates few-shot learning methods for open-set KWS classification by combining a deep feature encoder with a prototype-based classifier. With user-defined keywords from 10 classes of the Google Speech Command dataset, our study reports an accuracy of up to 76%...
In this paper, we conduct a comprehensive study on the impact of handcrafted and deep features from fingerprint images on the classification error rate of the fingerprint liveness detection task. We use LBP, LPQ and BSIF as handcrafted features and VGG-19 and Residual CNN as deep feature ...
We also report Open Set Classification Rate (OSCR) [12] measuring the trade-off between the two aspects. Implementation details. For the task model, the feature extractor includes a discrete cosine transform (DCT) trans- formation layer and a simple convolution networ...
SettingsCIFAR10CIFAR+10CIFAR+50TinyImageNetCIFAR100ImageNet-100ImageNet-200ImageNet-LT L=10, pos = front95.9695.8196.0888.9488.4898.8095.2676.84 L=10, pos = end94.2696.0795.5586.9888.5798.6796.5877.36 L=15, pos = mid94.3896.4095.4687.1489.5698.8495.7974.12 ...
Considering recognition as a target/non-target classification, TTR is equivalent to re- call and FTR to false positive rate. For evaluation, all test IDs are enrolled in the gallery. To compute the TTR, we count the probes correctly matched to their gallery images....
we can consider the binary classification of OOD as a noisy label problem. [27] showed that a DNN trained on noisy labeled datasets does not memorize noisy labels under a high learning rate. Thus, the noisy label of a sample can be corrected by reassigning the probability output of the DNN...
To address this limitation, the open-set categorization rate (OSCR) is introduced as a novel metric [34]. If δ is the threshold value, the correct classification rate (CCR) is defined as the fraction of correctly classified samples from the KCs where the correct class has a probability ...