Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Hierarchical Quantized Autoencoders
Authors: Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide qualitative and quantitative evaluations on the Celeb A and MNIST datasets. and 6 Experiments |
| Researcher Affiliation | Industry | Will Williams EMAIL Sam Ringer EMAIL John Hughes EMAIL Tom Ash EMAIL David Mac Leod EMAIL Jamie Dougherty EMAIL |
| Pseudocode | Yes | Algorithm 1 Lossy Compression Pseudo-code Using A Quantized Hierarchy |
| Open Source Code | Yes | Code available at https://github.com/speechmatics/hqa |
| Open Datasets | Yes | We provide qualitative and quantitative evaluations on the Celeb A and MNIST datasets. and provides citations [21] and [18] which point to the public datasets. E.g., for Celeb A: Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015. URL http:// mmlab.ie.cuhk.edu.hk/projects/Celeb A.html. |
| Dataset Splits | No | The paper mentions evaluating on 'Celeb A test examples' and 'MNIST 10k test samples' but does not provide specific training, validation, and test dataset split percentages or counts for reproducibility beyond the test set size. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various techniques and frameworks (e.g., VAEs, VQ-VAEs, Gumbel Softmax) but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | While training HQA, we linearly decay the Gumbel Softmax temperature to 0 so the soft quantization operation closely resembles hard quantization... and We control for the number of parameters (∼1M) in each system, training each with codebook size 256 and dimension 64. |