Understanding the Limitations of Conditional Generative Models
Authors: Ethan Fetaya, Joern-Henrik Jacobsen, Will Grathwohl, Richard Zemel
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we find that while we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. We relate this failure to various undesirable model properties that can be traced to the maximum likelihood training objective. |
| Researcher Affiliation | Academia | Vector Institute and University of Toronto {ethanf, jjacobs,wgrathwohl, zemel}@cs.toronto.edu |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. Methods are described in prose and mathematical equations. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | Yes | Experimentally, we find that while we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. We also model MNIST and CIFAR10 as is typically done in the literature. |
| Dataset Splits | No | The paper mentions using a 'test set' for evaluation (e.g., 'over the test set'), but it does not specify the exact percentages or counts for training, validation, and test splits, nor does it reference a specific standard split that details these proportions. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions 'pytorch' as a framework but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | The models are optimized using Adam, for 150 epochs. The initial learning rate is 1e 3, decayed by a factor of 10 every 60 epochs. For the reweighted optimization the objective is loss = log(p(x|y))/D log(p(y|x)) (6) where D is the data dimension (3x32x32 for CIFAR, 1x32x32 for MNIST). |