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].
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. |