Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning to Infer Graphics Programs from Hand-Drawn Images
Authors: Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We trained our network by sampling specs S and target images I for randomly generated scenes1 and maximizing Pθ[S|I], the likelihood of S given I, with respect to model parameters θ, by gradient ascent. We trained on 105 scenes... Experiment 1: Figure 4. To evaluate which components of the model are necessary to parse complicated scenes, we compared the neural network with SMC against the neural network by itself... Experiment 2: Figures 5 7. We evaluated, but did not train, our system on 100 real hand-drawn figures... Experiment 3: Table 5; Figure 8; Supplement Section 4. We compare synthesis times for our learned search policy with 4 alternatives... |
| Researcher Affiliation | Academia | Kevin Ellis MIT EMAIL Daniel Ritchie Brown University EMAIL Armando Solar-Lezama MIT EMAIL Joshua B. Tenenbaum MIT EMAIL |
| Pseudocode | No | The paper describes algorithms and presents a grammar (Table 2) but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code and data are available at https://github.com/ellisk42/Tik Z. |
| Open Datasets | Yes | We trained our network by sampling specs S and target images I for randomly generated scenes1... We trained on 105 scenes... We evaluated, but did not train, our system on 100 real hand-drawn figures... The code and data are available at https://github.com/ellisk42/Tik Z. |
| Dataset Splits | No | The paper mentions training on '10^5 scenes' and evaluating on '100 real hand-drawn figures' but does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | We trained on 105 scenes, which takes a day on an Nvidia Titan X GPU. |
| Software Dependencies | No | The paper mentions using tools like 'Sketch tool [1]' but does not provide specific version numbers for any software, libraries, or programming languages (e.g., Python, PyTorch) used in the experiments. |
| Experiment Setup | No | The paper states 'Supplement Section 1 gives the full details of the architecture and training of this network' but does not provide specific hyperparameter values, optimizer settings, or other detailed training configurations in the main text. |