M2T: Masking Transformers Twice for Faster Decoding
F Mentzer, E Agustsson, M Tschannen
[Google Research]
- 动机：提高神经图像压缩的效果，加速神经图像压缩模型的推理速度。
- 方法：利用预先定义的、确定性的时间表对掩码Transformer进行双掩码，从而加速模型并提高压缩效果。同时，使用掩码的注意力机制进行训练，并在推理过程中使用激活缓存技术，进一步提高模型的推理速度。
- 优势：相比之前的方法，这种新的神经图像压缩方法不需要使用多尺度模型，而是使用标准的Transformer应用于瓷砖，从而实现了更快速的推理和更好的压缩效果。此外，这种方法可以在固定、确定性的时间表下表现良好，进一步提高了模型的可靠性和实用性。
提出一种新的神经图像压缩方法，利用预先定义的、确定性的时间表对掩码Transformer进行双掩码，从而加速模型并提高压缩效果。
https://arxiv.org/abs/2304.07313 
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