We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sg-nm/Evolutionary-Autoencoders | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| arg_wrapper_mp | Not yet run | sg-nm/Evolutionary-Autoencoders/Denoising/cgp_config.py code served (permissive licence) · get_code("a376f4f1d756e187") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Researchers have applied deep neural networks to image restoration tasks, in which they proposed various network architectures, loss functions, and training methods. In particular, adversarial training, which is employed in recent studies, seems to be a key ingredient to success. In this paper, we show that simple convolutional autoencoders (CAEs) built upon only standard network components, i.e., convolutional layers and skip connections, can outperform the state-of-the-art methods which employ adversarial training and sophisticated loss functions. The secret is to employ an evolutionary algorithm to automatically search for good architectures. Training optimized CAEs by minimizing the $\ell_2$ loss between reconstructed images and their ground truths using the ADAM optimizer is all we need. Our experimental results show that this approach achieves 27.8 dB peak signal to noise ratio (PSNR) on the CelebA dataset and 40.4 dB on the SVHN dataset, compared to 22.8 dB and 33.0 dB provided by the former state-of-the-art methods, respectively.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.00370")
get_code_for_paper("1803.00370")
have("1803.00370")
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