benchmark_VAE
memorization
| benchmark_VAE | memorization | |
|---|---|---|
| 4 | 1 | |
| 1,695 | 5 | |
| - | - | |
| 6.1 | 10.0 | |
| about 1 month ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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benchmark_VAE
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Researchers From INRIA France Propose ‘Pythae’: An Open-Source Python Library Unifying Common And State-of-the-Art Generative AutoEncoder (GAE) Implementations
Continue reading | Checkout the paper, github
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[P] Pythae - Unifying generative autoencoder implementations in Python
Code for https://arxiv.org/abs/2206.08309 found: https://github.com/clementchadebec/benchmark_VAE
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[P] Python library for Variational Autoencoder benchmarking
Github link: https://github.com/clementchadebec/benchmark_VAE
- Python library for Variational Autoencoder Benchmarking
memorization
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[D] DALL·E to be made available as API, OpenAI to give users full ownership rights to generated images
Codex is not technically copy pasting; it is generating a new output that is (almost) exactly the same, or indistinguishable on the eyes of a human, to the input. Sounds like semantics, but there is no actual copying. You already have music generating algorithms that can also generate short samples that are indistinguishable to the inputs (memorisation). Dall-E 2 is not there yet, but we are close to prompting "Original Mona Lisa painting" and be given back the original Mona Lisa painting with striking similarities. There are already several generative models of images that can mostly memorise inputs used to train it (quick example found using google: https://github.com/alan-turing-institute/memorization).
What are some alternatives?
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