MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer
Authors: Chi Zhang, Yixin Zhu, Song-Chun Zhu1254-1261
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The qualitative and quantitative analysis in the experiments demonstrates that the proposed approach achieves high-quality arbitrary artistic style transfer effectively, with a good trade-off among speed, flexibility, and quality. Comprehensive experiments with both qualitative and quantitative analysis, compared with prior neural style transfer methods, demonstrate that the proposed method achieves a good trade-off among speed, flexibility, and quality. 5 Experiments |
| Researcher Affiliation | Academia | Chi Zhang, Yixin Zhu, Song-Chun Zhu International Center for AI and Robot Autonomy (CARA) {chizhang,yzhu,sczhu}@cara.ai |
| Pseudocode | Yes | Algorithm 1: Meta Style |
| Open Source Code | No | The paper mentions "additional videos are provided in the supplementary files," but does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We train our model using MS-COCO (?) as our content dataset and Wiki Art test set (?) as our style dataset. (Citations: Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Doll ar, P.; and Zitnick, C. L. 2014. Microsoft coco: Common objects in context. In Proceedings of European Conference on Computer Vision (ECCV). Nichol, K. 2016. Painter by numbers, wikiart.) |
| Dataset Splits | Yes | The inner objective uses a model initialized with θ and only optimizes contents in the training set, whereas the outer objective tries to generalize to contents in the validation set. The explicit training-validation separation in the framework forces the style transfer model to generalize to unobserved content images without over-fitting to the training set. Input : content training dataset Dtr, content validation dataset Dval, style dataset Dstyle, inner learning rate δ, outer learning rate η, number of inner updates T |
| Hardware Specification | Yes | The entire model is trained on a Nvidia Titan Xp with only 0.1 million iterations. which amounts to an additional 24 seconds of training time with a Titan Xp GPU. |
| Software Dependencies | No | The paper mentions optimizers like Adam and SGD but does not specify any software libraries or their version numbers (e.g., PyTorch 1.x, TensorFlow 2.x) that were used for implementation. |
| Experiment Setup | Yes | We use Adam (?) with a learning rate 0.001 to optimize the outer objective and vanilla SGD with a learning rate 0.0001 for the inner objective. All batches are of size 4. We fix α = 1, β = 1 105 across all the experiments. Content loss is computed on relu2_2 of a pre-trained VGG16 model and style loss over relu1_2, relu2_2, relu3_3 and relu4_3. To encourage fast adaptation, we constrain T = 1. The entire model is trained on a Nvidia Titan Xp with only 0.1 million iterations. |