Disentanglement via Latent Quantization
Authors: Kyle Hsu, William Dorrell, James Whittington, Jiajun Wu, Chelsea Finn
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the broad applicability of this approach by adding it to both basic data-reconstructing (vanilla autoencoder) and latent-reconstructing (Info GAN) generative models. For reliable evaluation, we also propose Info MEC, a new set of metrics for disentanglement that is cohesively grounded in information theory and fixes well-established shortcomings in previous metrics. Together with regularization, latent quantization dramatically improves the modularity and explicitness of learned representations on a representative suite of benchmark datasets. In particular, our quantized-latent autoencoder (QLAE) consistently outperforms strong methods from prior work in these key disentanglement properties without compromising data reconstruction. |
| Researcher Affiliation | Academia | Kyle Hsu Will Dorrell James C. R. Whittington Jiajun Wu Chelsea Finn Stanford University University College London Oxford University |
| Pseudocode | Yes | Algorithm 1 contains pseudocode for latent quantization and computing the quantization and commitment losses. Appendix A presents pseudocode for training a quantized-latent autoencoder (QLAE) in Algorithm 2 and a quantized-latent Info WGAN-GP [12, 1, 23] in Algorithm 3. |
| Open Source Code | Yes | Code for models and Info MEC metrics: https://github.com/kylehkhsu/latent_quantization. |
| Open Datasets | Yes | We benchmark on four established datasets: Shapes3D [9], MPI3D [20], Falcor3D [53], and Isaac3D [53]. |
| Dataset Splits | No | The paper states 'We train on the entire dataset then evaluate on 10,000 i.i.d. samples' which describes a test set, and mentions hyperparameter tuning, but does not explicitly provide details on a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper acknowledges the use of several open-source software packages (e.g., NumPy, JAX, scikit-learn) but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We fix the number of latents in all methods to twice the number of sources. For quantized-latent models, we fix nv = 10 discrete values per codebook. We tune one key regularization hyperparameter per method per dataset with a thorough sweep (Table 11, Appendix C.2). Appendices C.2 and C.3 provide specific values for learning rates, batch sizes, update steps, loss weights, and network architectures. |