Learning Group Actions on Latent Representations
Authors: Yinzhu Jin, Aman Shrivastava, Tom Fletcher
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our approach on five image datasets with diverse groups acting on them and demonstrate superior performance to recently proposed methods for modeling group actions. 6 Experiments We conduct experiments on five different image datasets. |
| Researcher Affiliation | Academia | Yinzhu Jin Department of Computer Science University of Virginia EMAIL Aman Shrivastava Department of Computer Science University of Virginia EMAIL P. Thomas Fletcher Department of Electrical and Computer Engineering Department of Computer Science University of Virginia EMAIL |
| Pseudocode | No | The paper provides architectural descriptions and figures but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The architecture and the training was implemented in Py Torch and the code will be made available upon publication. |
| Open Datasets | Yes | Rotated MNIST dataset is obtained by simply rotating images from MNIST dataset [6]... Brain MRI dataset is derived from the Human Connectome Project [11]. Neural 3D mesh renderer (NMR) [14] dataset has been used in multiple previous works in the field of novel view synthesis. This dataset is derived from Shape Net [3]... Plane in the sky dataset is our own rendering of Shape Net Core [3] airplane objects. |
| Dataset Splits | Yes | We use the original train-test split and image size of 28 28. (Rotated MNIST) among which 880 images are used for training and 233 for testing. (Brain MRI) We randomly split out 20% as the testing set. (Plane in the sky) We randomly split 1/8 of the training set as the validation set. |
| Hardware Specification | Yes | Our architecture was trained on 1 A100 GPU with a batch-size of 256 using the Adam optimizer. |
| Software Dependencies | No | All our experiments are implemented with the Py Torch [20] package. |
| Experiment Setup | Yes | Our architecture was trained on 1 A100 GPU with a batch-size of 256 using the Adam optimizer. The learning rate is 0.0001. |