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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Convolutional Inverse Graphics Network
Authors: Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present qualitative and quantitative tests of the model s efficacy at learning a 3D rendering engine for varied object classes including faces and chairs. |
| Researcher Affiliation | Collaboration | 1,2,4Massachusetts Institute of Technology, Cambridge, USA 3Microsoft Research, Cambridge, UK |
| Pseudocode | No | The paper describes the training procedure using numbered steps in Section 3.1 and references Figure 3, but it does not present a formal pseudocode or algorithm block labeled as such. |
| Open Source Code | No | The paper does not include any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We trained our model on about 12,000 batches of faces generated from a 3D face model obtained from Paysan et al. [17]... images of widely varied 3D chairs from many perspectives derived from the Pascal Visual Object Classes dataset as extracted by Aubry et al. [16, 1]. |
| Dataset Splits | Yes | We used approximately 1200 of these chairs in the training set and the remaining 150 in the test set; |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using the 'rmsprop [22] learning algorithm' but does not specify version numbers for any software libraries or dependencies (e.g., deep learning frameworks like TensorFlow or PyTorch, or Python versions). |
| Experiment Setup | Yes | We used the rmsprop [22] learning algorithm during training and set the meta learning rate equal to 0.0005, the momentum decay to 0.1 and weight decay to 0.01. |