Parallel Multiscale Autoregressive Density Estimation
Authors: Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando Freitas
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling. |
| Researcher Affiliation | Industry | 1Deep Mind. Correspondence to: Scott Reed <reedscot@google.com>. |
| Pseudocode | No | The paper describes the model architecture and process in text and diagrams (Figure 2, Figure 3) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code release or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our model on Image Net (Deng et al., 2009), Caltech-UCSD Birds (CUB) (Wah et al., 2011), the MPII Human Pose dataset (MPII) (Andriluka et al., 2014), the Microsoft Common Objects in Context dataset (MS-COCO) (Lin et al., 2014), and the Google Robot Pushing dataset (Finn et al., 2016). |
| Dataset Splits | Yes | There are 50, 000 training sequences and a validation set with the same objects but di erent arm trajectories. One test set involves a subset of the objects seen during training and another involving novel objects, both captured on an arm and camera viewpoint not seen during training. |
| Hardware Specification | Yes | Table 4. Sampling speed of several models in seconds per frame on an Nvidia Quadro M4000 GPU. |
| Software Dependencies | No | The paper mentions using 'Tensor Flow' for image resizing ('tf.image.resize_images') but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | All models for Image Net, CUB, MPII and MS-COCO were trained using RMSprop with hyperparameter = 1e 8, with batch size 128 for 200K steps. The learning rate was set initially to 1e 4 and decayed to 1e 5. |