示意图：上面一行是自编码器，输入图经可学习的编码器压成一个不可读的潜向量，再由可学习的解码器还原成重建图。下面一行是代码自编码，目标图经优化求解拟合出一段可读、可执行的代码，再由固定的渲染器执行这段代码，画回重建图。三处不同：编码靠优化求解而不是学习，中间表示是代码而不是向量，解码器是固定的渲染器而不是可学习的网络。 Diagram. Top lane, an autoencoder: an input image goes through a learned encoder into an unreadable latent vector, and a learned decoder turns it back into a reconstruction. Bottom lane, code autoencoding: code is fitted to the target image by optimization, giving a readable, executable program, and a fixed renderer executes that code to draw the reconstruction. Three differences: the encoder is solved rather than learned, the intermediate is code rather than a vector, and the decoder is a fixed renderer rather than a learned network.

自编码器Autoencoder

输入图Input image

编码器Encoder 可学习learned

潜表示Latent一个向量，不可读a vector, not readable

解码器Decoder 可学习learned

重建图Reconstruction

同样输入一张图same input

编码Encode学出来 → 解出来learned → solved

中间表示Middle向量 → 代码vector → code

解码Decode可学习 → 固定learned → fixed

同样画回原图same goal

代码自编码Code autoencoding

目标图Target

代码拟合Code fitting 优化求解solved by optimization

代码Code可读、可执行readable and executable

执行代码Execute the code 固定渲染器fixed renderer

重建图Reconstruction