Robert Bamler, Tim Xiao
We lifted 10 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| timxzz/hit | canonical | 9 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| ResNet18 | Ran | timxzz/hit/svhn_classifier.py code served (permissive licence) · get_code("a7e48b90e47310fe") |
| beta_pair_to_rgb | Ran | timxzz/hit/plot_eval.py code served (permissive licence) · get_code("a18050f6825402cd") |
| betas_to_rgb | Ran | timxzz/hit/plot_eval.py code served (permissive licence) · get_code("6acdec96431c470d") |
| get_runs_list_from_batch_dir | Ran | timxzz/hit/utils_eval.py code served (permissive licence) · get_code("a5fd48f12bc66a01") |
| get_single_run | Ran | timxzz/hit/utils_eval.py code served (permissive licence) · get_code("3eb470d3342c5927") |
| gumbel_softmax_sample | Ran | timxzz/hit/utils.py code served (permissive licence) · get_code("d39a16dc280db7c4") |
| kl_categorical | Ran | timxzz/hit/utils.py code served (permissive licence) · get_code("3042b77f69662558") |
| mi_bound_given_acc | Ran | timxzz/hit/plot_eval.py code served (permissive licence) · get_code("3a062a2eadfae8ba") |
| sample_gumbel | Ran | timxzz/hit/utils.py code served (permissive licence) · get_code("2dde33af7b189247") |
| load_data | Not yet run | timxzz/hit/dataloader.py code served (permissive licence) · get_code("0f7c331072af4dbf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Variational Autoencoders (VAEs) were originally motivated (Kingma & Welling, 2014) as probabilistic generative models in which one performs approximate Bayesian inference. The proposal of β-VAEs (Higgins et al., 2017) breaks this interpretation and generalizes VAEs to application domains beyond generative modeling (e.g., representation learning, clustering, or lossy data compression) by introducing an objective function that allows practitioners to trade off between the information content ("bit rate") of the latent representation and the distortion of reconstructed data (Alemi et al., 2018). In this paper, we reconsider this rate/distortion trade-off in the context of hierarchical VAEs, i.e., VAEs with more than one layer of latent variables. We identify a general class of inference models for which one can split the rate into contributions from each layer, which can then be tuned independently. We derive theoretical bounds on the performance of downstream tasks as functions of the individual layers' rates and verify our theoretical findings in large-scale experiments. Our results provide guidance for practitioners on which region in rate-space to target for a given application.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.04855")
get_code_for_paper("2302.04855")
have("2302.04855")
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