Generating Images with Perceptual Similarity Metrics based on Deep Networks
Authors: Alexey Dosovitskiy, Thomas Brox
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate two examples of use cases of the proposed loss: (1) networks that invert the Alex Net convolutional network; (2) a modified version of a variational autoencoder that generates realistic high-resolution random images. |
| Researcher Affiliation | Academia | Alexey Dosovitskiy and Thomas Brox University of Freiburg |
| Pseudocode | No | The paper describes model architectures and training procedures in text and tables but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to open-source code or an explicit statement that the code is publicly available. |
| Open Datasets | Yes | We trained on 227 227 pixel crops of images from the ILSVRC-2012 training set |
| Dataset Splits | Yes | We trained on 227 227 pixel crops of images from the ILSVRC-2012 training set and evaluated on the ILSVRC-2012 validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU or CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | We modified the caffe [23] framework to train the networks. For optimization we used Adam [25]... (Mentions software and optimizer names, but no version numbers for Caffe or specific libraries). |
| Experiment Setup | Yes | Coefficients for adversarial and image loss were respectively λadv = 100, λimg = 2 10 6. The feature loss coefficient λfeat depended on the comparator being used. It was set to 0.01 for the Alex Net CONV5 comparator... For optimization we used Adam [25] with momentum β1 = 0.9, β2 = 0.999 and initial learning rate 0.0002... We used batch size 64 in all experiments. The networks were trained for 500, 000-1, 000, 000 mini-batch iterations. In all networks we use leaky Re LU nonlinearities, that is, LRe LU(x) = max(x, 0) + α min(x, 0). We used α = 0.3 in our experiments. |