GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Authors: Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, Ingmar Posner
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train GENESIS on several publicly available datasets and evaluate its performance on scene generation, decomposition, and semi-supervised learning. ... We show both qualitatively and quantitatively that in contrast to prior art, GENESIS is able to generate coherent scenes while also performing well on scene decomposition. |
| Researcher Affiliation | Academia | Martin Engelcke , Adam R. Kosiorek , Oiwi Parker Jones & Ingmar Posner / Applied AI Lab, University of Oxford; Dept. of Statistics, University of Oxford |
| Pseudocode | No | The paper describes the model architecture and training process, but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models are available at https://github.com/applied-ai-lab/genesis. |
| Open Datasets | Yes | We conduct experiments on three canonical and publicly available datasets: coloured Multi-d Sprites (Burgess et al., 2019), the GQN dataset (Eslami et al., 2018), and Shape Stacks (Groth et al., 2018). ... Multi-d Sprites (Burgess et al., 2019) ... available at https://github.com/deepmind/dsprites-dataset. ... GQN (Eslami et al., 2018) ... It can be downloaded from https://github.com/deepmind/gqn-datasets. ... Shape Stacks (Groth et al., 2018) ... download links can be found at https://shapestacks.robots.ox.ac.uk/. |
| Dataset Splits | Yes | We set aside 10,000 for validation and testing each. (referring to Multi-d Sprites) |
| Hardware Specification | No | The paper mentions general computing resources like "University of Oxford Advanced Research Computing (ARC) facility" and "Hartree Centre resources," and states "training GENESIS takes about two days on a single GPU," but does not provide specific GPU models, CPU models, or detailed hardware specifications. |
| Software Dependencies | No | The paper mentions several software components and algorithms used (e.g., LSTM, ELUs, GECO, ADAM optimiser), but it does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | We use an image resolution of 64-by-64 for all experiments. The number of components is set to K = 5, K = 7, and K = 9 for Multi-d Sprites, GQN, and Shape Stacks, respectively. ... The scalar standard deviation of the Gaussian image likelihood components is set to σx = 0.7. ... The goal for the reconstruction error is set to 0.5655, multiplied by the image dimensions and number of colour channels. ... GECO hyperparameters, the default value of α = 0.99 is used and the step size for updating β is set to 10 5. ... All models are trained for 5 105 iterations with a batch size of 32 using the ADAM optimiser (Kingma & Ba, 2015) and a learning rate of 10 4. |