Learning Generative Models with Visual Attention
Authors: Charlie Tang, Nitish Srivastava, Ruslan Salakhutdinov
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We used two face datasets in our experiments. The first dataset is a frontal face dataset, called the Caltech Faces from 1999... We also used the CMU Multi-PIE dataset [30]... Fig. 5 shows the quantitative results of Intersection over Union (IOU) of the ground truth face box and the inferred face box... Table 1: Face localization accuracy... Table 2 shows the estimates of the variational lower-bounds on the average log-density... |
| Researcher Affiliation | Academia | Department of Computer Science University of Toronto Toronto, Ontario, Canada {tang,nitish,rsalakhu}@cs.toronto.edu |
| Pseudocode | No | No structured pseudocode or algorithm block is explicitly labeled or presented in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We used two face datasets in our experiments. The first dataset is a frontal face dataset, called the Caltech Faces from 1999, collected by Markus Weber. ... We also used the CMU Multi-PIE dataset [30], which contains 337 subjects, captured under 15 viewpoints and 19 illumination conditions in four recording sessions for a total of more than 750,000 images. |
| Dataset Splits | Yes | We split the Caltech dataset into a training and a validation set. For the CMU faces, we first took 10% of the images as training cases for the Conv Net for approximate inference. The remaining 90% of the CMU faces are split into a training and validation set. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific hyperparameter values or detailed system-level training configurations in the main text. |