We present a novel approach to enhance the capabilities of VQVAE models through the integration of an Attentive Residual Encoder (AREN) and a Residual Pixel Attention layer. The objective of our research is to improve the performance of VQVAE 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 VQVAEs even more suitable for a wide range of applications.

翻译：我们提出了一种新颖的方法，通过集成注意力残差编码器（AREN）和残差像素注意力层来增强VQ-VAE模型的能力。本研究的目标是在保持实用参数水平的同时提升VQ-VAE的性能。AREN编码器设计为可在多个层级高效运行，适应多样化的架构复杂度。其关键创新在于将像素间自注意力机制融入AREN编码器，从而有效捕获并利用潜向量之间的上下文信息。此外，我们的模型通过增加编码层级进一步增强了表征能力。注意力层采用最小化参数策略，确保仅在获得其他像素相关有效信息时才修改潜向量。实验结果表明，我们提出的改进方案显著提升了数据表征与生成性能，使VQ-VAE更适用于广泛的应用场景。