Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
Authors: Alireza Nasiri, Tristan Bepler
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In comprehensive experiments, we show that TARGET-VAE learns disentangled representations without supervision that significantly improve upon, and avoid the pathologies of, previous methods. |
| Researcher Affiliation | Academia | Alireza Nasiri Simons Machine Learning Center New York Structural Biology Center anasiri@nysbc.org Tristan Bepler Simons Machine Learning Center New York Structural Biology Center tbepler@nysbc.org |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Our code is available at https://github.com/SMLC-NYSBC/TARGET-VAE. |
| Open Datasets | Yes | Datasets We use two variants of the MNIST dataset: 1) (MNIST(N)) MNIST digits... and 2) (MNIST(U)) MNIST digits... (Appendix Figure 1). ... EMPIAR-10025 [34]... EMPIAR-10029 is a simulated EM dataset of Gro EL particles. |
| Dataset Splits | Yes | In both datasets, the train and test sets have 60,000, and 10,000 images of dimensions 50x50 pixels, respectively. ... We randomly select 10% of the images as the validation set to monitor the training process. |
| Hardware Specification | Yes | We run all our experiments in a single NVIDIA A100 GPU, with 80 GB memory. |
| Software Dependencies | No | The paper mentions specific optimizers and techniques used (e.g., 'ADAM optimizer', 'Gumbel-Softmax') but does not specify version numbers for any programming languages, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | We use leaky-ReLU activation functions, and the batch size for all the experiments is set to 100. We use ADAM optimizer [30], with learning rate of 2e-4, and the learning rate is decayed by the factor of 0.5 after no improvements in the loss for 10 epochs. We run the training for a maximum of 500 epochs with early-stopping in case of no improvements in the loss for 20 epochs. |