Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
Authors: Reinhard Heckel and Mahdi Soltanolkotabi
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We depict this phenomena in Figure 1 where we fit a randomly initialized over-parameterized convolutional generator to an image via running gradient descent on the objective L(C) = G(C) y 2 2. Here, G(C) is the convolutional generator with weight parameters C, and y is either a noisy image, a clean image, or noise. This experiment demonstrates that an over-parameterized convolutional network fits a natural image (Figure 1b) much faster than noise (Figure 1c). |
| Researcher Affiliation | Academia | Reinhard Heckel Dept. of Electrical and Computer Engineering Technical University of Munich reinhard.heckel@tum.de Mahdi Soltanolkotabi Dept. of Electrical and Computer Engineering University of Southern California soltanol@usc.edu |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | CODE AND ACKNOWLEDGEMENTS Code to reproduce the experiments is available at https://github.com/MLI-lab/ overparameterized_convolutional_generators. |
| Open Datasets | Yes | We compared the performance of those four methods on denoising 100 randomly chosen images from the Image Net validation set. |
| Dataset Splits | No | The paper describes fitting a single noisy image rather than traditional training/validation/test dataset splits. Appendix A describes evaluation on 100 images from the ImageNet validation set, which serve as evaluation data, not for traditional splitting for a supervised learning setup. |
| Hardware Specification | No | R. Heckel is partially supported by NSF award IIS-1816986, acknowledges support of the NVIDIA Corporation in form of a GPU, and would like to thank Tobit Klug for proofreading a previous version of this manuscript. The mention of 'a GPU' is too general and does not provide specific model or configuration details. |
| Software Dependencies | No | The paper mentions "pytorch and tensorflow" as standard implementations for upsampling operations, but does not provide specific version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | early stopped after 1900 iterations, again with the same stopping time as proposed in the original paper, iii) an under-parameterized deep decoder with five layers and k = 128 channels in each layers, trained for 3000 iterations (which is close to convergence), iv) an over-parameterized deep decoder with five layers and k = 512 channels in each layer, early stopped at 1500 iterations. |