Using an OC-SVM for the classification task, several experiments were done using publicly available image and video datasets ... proposed method was compared with a baseline Convolutional Autoencoder ...

ASD discrimination method based on generated images: Using the generated MRI images ... issues of data sparsity and label imbalance. By combining autoencoder generation and deep learning ...

In image classification tasks using ViTs and CNNs, the softmax function is commonly employed to represent the output as a probability value. However, these outputs are often poorly calibrated, making ...

In contrast, we propose a novel method that operates directly on the latent space of a generative model, specifically a Diffusion Autoencoder (DAE ... Reconstruct the image by calling model.render() ...

Deep Learning models are implemented using ... classification accuracy; the used architecture is similar to LeNet. That is probably because the dataset is very easy: small images with relatively few, ...

In this study, the deep wavelet autoencoder combined with DNN helps to ... in the “Feature-Based Classification” of the section “Image-based classification” are combined using LSF combiner. The LSF ...

To use an autoencoder for anomaly detection, you compare the reconstructed version of an image with its source input ... because binary cross entropy loss is intended for binary classification ...