Unsupervised Learning of Compositional Energy Concepts
Authors: Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, Igor Mordatch
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We quantitatively and qualitatively show that COMET can recover the global factors of variation in an image in Section 5.1, as well as the local factors in an image in Section 5.2. Furthermore, we show that the components captured by COMET can generalize well, across separate modalities in Section 5.3. and Finally, we evaluate the learned representations on disentanglement. In Falcor3D [40], each image corresponds to a combination of 7 factors of variation; lighting intensity, lighting x, y & z direction, and camera x, y & z position. We consider three commonly used metrics for evaluation, the Beta VAE metric [23], the Mutual Information Gap (MIG) [7], and the Mean Correlation Coefficient (MCC) [25]. |
| Researcher Affiliation | Collaboration | Yilun Du MIT CSAIL yilundu@mit.edu Shuang Li MIT CSAIL lishuang@mit.edu Yash Sharma University of Tübingen yash.sharma@uni-tuebingen.de Joshua B. Tenenbaum MIT CSAIL, BCS, CBMM jbt@mit.edu Igor Mordatch Google Brain imordatch@google.com |
| Pseudocode | Yes | We provide pseudocode for training our model in Algorithm 1. |
| Open Source Code | Yes | *Code and data available at https://energy-based-model.github.io/comet/ |
| Open Datasets | Yes | We assess the ability of COMET to decompose global factors of variation in scenes consisting of lighting and camera illumination from Falcor3D (NVIDIA high-resolution disentanglement dataset) [40], scene factors of variation in CLEVR [29], and face attributes in real images from Celeb A-HQ [30]. |
| Dataset Splits | No | No explicit training/test/validation dataset splits (e.g., percentages, counts, or specific split methods) are provided in the paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names like PyTorch, TensorFlow, or specific Python versions) are provided in the paper. |
| Experiment Setup | No | The paper mentions general experimental settings like 'latent dimension of 64' or 'small latent dimension (16)' and that 'additional training algorithm and model architecture details' are in the appendix, but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |