Wei-Lun Chao, Hong-You Chen, Cheng-Hao Tu, David Carlyn
We lifted 4 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| andytu28/LearningFractals | — | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| forward_pass_iterate_svdformat | Ran | andytu28/LearningFractals/deep_fractal.py code served (permissive licence) · get_code("d516f571fdb9d435") |
| make_diagnal_matrix | Ran | andytu28/LearningFractals/deep_fractal.py code served (permissive licence) · get_code("b604b8cad31eeac0") |
| make_matrices_from_svdformat | Ran | andytu28/LearningFractals/deep_fractal.py code served (permissive licence) · get_code("1067f8586cbb176d") |
| make_rotation_matrix | Not yet run | andytu28/LearningFractals/deep_fractal.py code served (permissive licence) · get_code("a38980a23a258ca0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Fractals are geometric shapes that can display complex and self-similar patterns found in nature (e.g., clouds and plants). Recent works in visual recognition have leveraged this property to create random fractal images for model pre-training. In this paper, we study the inverse problem -given a target image (not necessarily a fractal), we aim to generate a fractal image that looks like it. We propose a novel approach that learns the parameters underlying a fractal image via gradient descent. We show that our approach can find fractal parameters of high visual quality and be compatible with different loss functions, opening up several potentials, e.g., learning fractals for downstream tasks, scientific understanding, etc.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.12722")
get_code_for_paper("2303.12722")
have("2303.12722")
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