A Multi-Implicit Neural Representation for Fonts
Authors: Pradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher, Hailin Jin, Niloy Mitra
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate the proposed representation for various tasks including reconstruction, interpolation, and synthesis to demonstrate clear advantages with existing alternatives. |
| Researcher Affiliation | Collaboration | 1University College London 2Adobe Research |
| Pseudocode | No | The paper describes methods in text and provides diagrams, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about code release or links to source code repositories. |
| Open Datasets | Yes | For a fair comparison, we train all the algorithms on the same dataset used by Im2Vec [20], which consists of 12,505 images. ... We train on 1,000 font families, i.e., 52,000 images, and test on 100 font families. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly describe a separate validation split with specific percentages or sample counts for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For local corner template loss, we first perform corner detection. A corner is defined as a local where two curves intersect at an angle less than a threshold (the threshold is 3rad or 171 in our experiments). The template size is 7 × 7 corresponding to the image size of 128 × 128. ... we set the initial anti-aliasing range to be the whole image range and slowly shrink it to k w 1 during the training, where w is image width, and k = 4 in our experiments. |