Code to accompany the paper "Hierarchical Quantized Autoencoders"
$ ./setup.sh
$ source venv_hqa/bin/activate
$ jupyter lab # open hqa.ipynb
Code to accompany the paper "Hierarchical Quantized Autoencoders"
License: MIT License
Hi!
I have a question regarding how the quantized representation can be compressed in practice. In particular, I understand that entropy is used as an estimation of the "size" the compressed file should have. But how such file can be obtained at inference time? I have seen related work using, for example, lossless entropy coding algorithms or huffman coding, among others.
Thanks for your help!
Hi authors, thanks a lot for sharing the code.
I want to understand for HQA for MNIST, what does the hidden units for each layer denote?
In the code, I see the number of hidden units for each layer's encoder is [16, 16, 32, 64, 128]. I understand this to be the number of output channels of the middle layer of each 3-layer feedforward encoder network?
For decoder, its the number of output channels of the first layer in the decoder after quantization layer, which is [16, 64, 256, 512, 1024].
Is this the case?
Secondly, in Table 10 from the paper, the number of hidden layers units for each layer's encoder is [16, 16, 32, 48, 80], while for decoders, it's [16, 32, 48, 80, 128]. These don't match the numbers in the code, am I reading them incorrectly? Thanks!
Hey.
I am newbie in computer vision research. I want this code to try on face image reconstruction as you have mentioned in paper. I find it difficult to understand the paper in the first attempt. Any help or suggestions ?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.