Learning Group Actions on Latent Representations
Authors: Yinzhu Jin, Aman Shrivastava, Tom Fletcher
NeurIPS 2024 | Conference 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 yj3cz@virginia.edu Aman Shrivastava Department of Computer Science University of Virginia as3ek@virginia.edu P. Thomas Fletcher Department of Electrical and Computer Engineering Department of Computer Science University of Virginia ptf8v@virginia.edu |
| 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. |