Annotation is a labor-intensive task in deep learning, which requires large amounts of training data. In active learning, which reduces the annotation work, the performance of the model is improved without annotating all the data by performing annotation step by step. In this study, we propose a method to incorporate a curriculum learning framework into active learning, which improves the performance of the model by learning from samples that are easy to identify. The experimental results show that the proposed method achieves 20% reduction in the total annotations compared to random sampling on CIFAR-10.
Anomaly detection is an essential task within an industry domain, and sophisticated approaches have been proposed. PaDiM has a promising direction, utilizing ImageNet-pretrained convolutional neural networks without expensive training costs. However, the cues and biases utilized by PaDiM, i.e., shape-vs-texture bias in an anomaly detection process, are unclear. To reveal the bias, we proposed to apply frequency analysis to PaDiM. For frequency analysis, we use a Fourier Heat Map that investigates the sensitivity of the anomaly detection model to input noise in the frequency domain. As a result, we found that PaDiM utilizes texture information as a cue for anomaly detection, similar to the classification models. Based on this preliminary experiment, we propose a shape-aware Stylized PaDiM. Our model is a PaDiM that uses pre-trained weights learned on Stylized ImageNet instead of ImageNet. In the experiments, we confirmed that Stylized PaDiM improves the robustness of high-frequency perturbations. Stylized PaDiM also achieved higher performance than PaDiM for anomaly detection in clean images of MVTecAD.
KEYWORDS: Correlation coefficients, Education and training, Data modeling, Visualization, Matrices, Machine learning, Deep learning, Factor analysis, Information visualization, Image analysis
Detailed identification of visual impressions of objects by attributes can be leveraged to develop products and improve customer satisfaction. In this study, we propose a method to estimate Kansei (affective) information for each attribute, which is the visual impression received from the image. For each attribute, we created a dataset with Kansei indices. By fine-tuning the created dataset to combine attribute information with the output of ResNet18 which was already trained with ImageNet to output indexes, we confirmed that the correlation coefficients for multiple item ratings were higher than those of a deep learning model without attribute information.
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