Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Authors: Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns, Chelsea Finn, Jiajun Wu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark on four established image datasets with ground-truth source labels that facilitate quantitative evaluation |
| Researcher Affiliation | Academia | 1Stanford University. Correspondence to: Kyle Hsu <kylehsu@cs.stanford.edu>. |
| Pseudocode | Yes | Algorithm 1 Pseudocode for the Tripod objective. |
| Open Source Code | Yes | Code is available at https://github.com/ kylehkhsu/tripod. |
| Open Datasets | Yes | We benchmark on four established image datasets with ground-truth source labels that facilitate quantitative evaluation: Shapes3D (Burgess & Kim, 2018), MPI3D (Gondal et al., 2019), Falcor3D (Nie, 2019), and Isaac3D (Nie, 2019). |
| Dataset Splits | Yes | We follow prior work in considering a statistical learning problem: we use the entire dataset for unsupervised training and evaluate on a subset of 10, 000 samples (Locatello et al., 2019). |
| Hardware Specification | No | The paper includes a 'Profiling Study' section that measures training iteration runtime, but does not provide specific hardware details such as GPU/CPU models, memory, or detailed computer specifications used for experiments. |
| Software Dependencies | No | The paper acknowledges developers of 'Num Py (Harris et al., 2020), JAX (Bradbury et al., 2018), Equinox (Kidger & Garcia, 2021), and scikit-learn (Pedregosa et al., 2011)', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Table 4: Fixed hyperparameters for all autoencoder variants (e.g., number of latents nz, Adam W learning rate, batch size). Table 5: Key regularization hyperparameter tuning done for each autoencoder (e.g., β, weight decay, λvanilla Hessian penalty, λlatent multiinformation). |