  We present a novel approach to enhance the capabilities of VQ-VAE models
through the integration of a Residual Encoder and a Residual Pixel Attention
layer, named Attentive Residual Encoder (AREN). The objective of our research
is to improve the performance of VQ-VAE while maintaining practical parameter
levels. The AREN encoder is designed to operate effectively at multiple levels,
accommodating diverse architectural complexities. The key innovation is the
integration of an inter-pixel auto-attention mechanism into the AREN encoder.
This approach allows us to efficiently capture and utilize contextual
information across latent vectors. Additionally, our models uses additional
encoding levels to further enhance the model's representational power. Our
attention layer employs a minimal parameter approach, ensuring that latent
vectors are modified only when pertinent information from other pixels is
available. Experimental results demonstrate that our proposed modifications
lead to significant improvements in data representation and generation, making
VQ-VAEs even more suitable for a wide range of applications as the presented.
