Exploring the Latent Space of Autoencoders with Interventional Assays
Authors: Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments & Results. Experimental Setup We apply our new tools on a small selection of common benchmark datasets, including 3D-Shapes [66], MNIST [67], and Fashion-MNIST [68] 3. Our methods are directly applicable to any VAE-based model, and can readily be extended to any autoencoders. Nevertheless, we focus our empirical evaluation on vanilla VAEs and some β-VAEs (denoted by substituting the β, so 4-VAE refers to a β-VAE where β = 4). Specifically, here we mostly analyze a 4-VAE model with a d = 24 latent space trained on 3D-Shapes (except for table 1) referred to as Model A, and include results on a range of other models in the appendix. All our models use four convolution and two fully-connected layers in the encoder and decoder. The models are trained using Adam [69] with a learning rate of 0.001 for 100k steps (see appendix for details). |
| Researcher Affiliation | Academia | Email: fleeb@tuebingen.mpg.de Max Planck Institute for Intelligent Systems, Tübingen, Germany |
| Pseudocode | No | The paper describes processes and derivations mathematically and conceptually, but it does not include any formally structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code at https://github.com/felixludos/latent-responses. |
| Open Datasets | Yes | We apply our new tools on a small selection of common benchmark datasets, including 3D-Shapes [66], MNIST [67], and Fashion-MNIST [68] 3. |
| Dataset Splits | No | The paper mentions using benchmark datasets for empirical evaluation and discusses 'training samples' in a toy example, but it does not specify explicit train/validation/test split percentages or sample counts for the main experiments, nor does it refer to predefined splits with citations. |
| Hardware Specification | No | The paper details the model architecture and training procedure ('All our models use four convolution and two fully-connected layers in the encoder and decoder. The models are trained using Adam [69] with a learning rate of 0.001 for 100k steps'), but it does not specify any hardware components such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam [69]' as the optimizer and that models are built with 'four convolution and two fully-connected layers', implying the use of deep learning frameworks, but it does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | All our models use four convolution and two fully-connected layers in the encoder and decoder. The models are trained using Adam [69] with a learning rate of 0.001 for 100k steps (see appendix for details). |