Learning FRAME Models Using CNN Filters
Authors: Yang Lu, Song-Chun Zhu, Ying Wu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Image generation experiments In our experiments, we use VGG filters (Simonyan and Zisserman 2015), and we use the Matlab code of Mat Conv Net (Vedaldi and Lenc 2014). Experiment 1: generating object patterns. We learn the non-stationary FRAME model (5) from images of aligned objects. The images are collected from the internet. |
| Researcher Affiliation | Academia | Yang Lu, Song-Chun Zhu, Ying Nian Wu Department of Statistics, University of California, Los Angeles, USA |
| Pseudocode | Yes | Algorithm 1 Learning and sampling algorithm |
| Open Source Code | Yes | The code, data, and more experimental results can be found at http://www.stat.ucla.edu/ yang.lu/project/deepFrame/main.html |
| Open Datasets | Yes | The code, data, and more experimental results can be found at http://www.stat.ucla.edu/ yang.lu/project/deepFrame/main.html |
| Dataset Splits | No | No specific details on train/validation/test splits are provided in the paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab code of Mat Conv Net', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For each category, the number of training images is around 10. We use M = 16 parallel chains for Langevin sampling. The number of Langevin iterations between every two consecutive updates of the parameters is L = 100. In the first scenery experiment, we learn 10 filters at the 4th convolutional layer (without local max pooling), based on the pre-trained VGG filters at the 3rd convolutional layer. The size of each Conv4 filter to be learned is 11 11 256. In the sunflower and egret experiments, we learn 20 filters of size 7 7 256 (with local max pooling). |