The decoder needs to convert from a narrow representation to a wide, reconstructed image. For example, the representation could be a 7x7x4 max-pool layer. This is the output of the encoder, but also ...

The encoder and decoder models are implemented with custom transformer layers and batch normalization, and the model is trained with the AdamW optimizer and early stopping for better convergence. The ...

Acquiring a substantial amount of high-quality data for industrial image detection poses significant challenges in the field of computer vision. The imbalance between normal and anomalous samples, ...

AI IMAGE RECONSTRUCTION - Our Hamlyn researchers proposed a new AI unsupervised deep learning framework for image reconstruction, ... A novel generative densely connected encoder-decoder architecture ...

Additionally, we will analyze the results produced by state-of-the-art frameworks that employ encoder-decoder methods. Let’s dive in. Single-View 3D Object Reconstruction. Single-view 3D object ...

Building on this exploration, ViTok is designed as a lightweight auto-encoder that achieves competitive performance with state-of-the-art auto-encoders on ImageNet-1K and COCO reconstruction tasks ...

This paper proposes a learning-based approach for reconstruction of global illumination with very low sampling budgets (as low as 1 spp) at interactive rates. At 1 sample per pixel (spp), the Monte ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

We propose a method for anomaly localization in industrial images using Transformer Encoder-Decoder Mask Reconstruction. The self-attention mechanism of the Transformer enables better attention to ...