This study presents a two-stage method utilizing EEG-derived features for deep representation learning, demonstrating its generalizability across multiple datasets and achieving superior results in EEG-based image synthesis, including image reconstruction and classification tasks, outperforming existing GAN-based methods.

This work introduces a novel approach using differentiable rendering to deform hand models, enabling the creation of meaningful and consistent shadows resembling desired target images.

This work proposes a tree-structured autoencoder framework to generate robust embeddings of point clouds by utilizing hierarchical information using graph convolution. We perform multiple experiments to assess the quality of embeddings generated by the proposed encoder architecture and visualize the t-SNE map to highlight its ability to distinguish between different object classes.

We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning.

We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128x128 images using a small number of images.

To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. We introduce APEX-1M, a new large scale dataset which contains both the plot images and the raw data.

We propose a large scale dataset of 1 Million challenging images for the task of hand written document image binarisation (HDIB) with accurate segmentation groundtruth.

We perform image enhancement using a deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework (DILIE) enhances content features and style features and preserves semantics in the enhanced image.

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Last updated July 2023.