karpathy / deep-vector-quantization

I believe that there is at least one 1x1 conv missing. In the paper on p. 3 they mention the crucial importance of those but I could only find a projection here prior to the bottleneck.

deep-vector-quantization/dvq/model/quantize.py

Line 93 in c3c026a

self.proj = nn.Conv2d(num_hiddens, n_embed, 1)

As a side question: What is the reason that many Autoencoder architectures do away completely with normalization layers in both the encoder and the decoder? I tried to reseach this question but couldnt find a proper answer. Also does the size and complexity of both directly relate to the reconstruction quality? I have seen huge encoder/decoder structures which did not perform significantly better than the modest form you have in this repo or Phils simple architecture for that matter

Missing 1x1 convolutions at the beginning of the decoder