Finding an Unsupervised Image Segmenter in each of your Deep Generative Models
Authors: Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five segmentation datasets across twelve different GANs demonstrate the effectiveness and generalizability of our approach. |
| Researcher Affiliation | Academia | University of Oxford {lukemk,chrisr,iro,av}@robots.ox.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 2 provides a pipeline diagram, not pseudocode. |
| Open Source Code | Yes | We upload code to the Supplementary Material to fully reproduce all experiments. This code contains a README file with a detailed description of the code structure, which should help enable others to reproduce and later extend upon our work. |
| Open Datasets | Yes | To demonstrate the efficacy of our method across resolutions and datasets, we implement both, GANs trained on Image Net (Deng et al., 2009) at a resolution of 128px, and GANs trained on the smaller Tiny Image Net dataset (100,000 images split into 200 classes) at a resolution of 64px. All experiments performed across all GANs utilize the same set of hyperparameters for both optimization and segmentation. |
| Dataset Splits | No | The paper mentions using standard evaluation datasets (e.g., CUB, Flowers) but does not specify explicit validation splits for these datasets. For training, they use GAN-generated data, which is essentially infinite and doesn't require fixed splits. |
| Hardware Specification | No | The paper states, 'Our results do not require extremely large amounts of compute; they can be reproduced with a single GPU by researchers with computational constraints.' This is not specific enough to identify the hardware used. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and UNet architecture, but does not provide specific version numbers for any software libraries or dependencies (e.g., PyTorch version, TensorFlow version). |
| Experiment Setup | Yes | We generate latent codes z N(0, 1) and optimize the vector vl (or vd) by gradient descent with the Adam (Kingma & Ba, 2014) optimizer and learning rate 0.05. We use λ = 5 for the light direction vl and λ = 5 for the dark direction vb. We perform 1000 optimization steps, by which point vl (or vd) has converged. [...] we train for 12000 steps using Adam with learning rate 10 3 and batch size 95, decaying the learning rate by a factor of 0.2 at iteration 8000. |