Search Results for "good"

...For a more detailed overview, you can read the paper or see a brief overview. After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis. By default, the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence.

...Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively. In order to colorize your own video, it requires to extract the video frames, and provide a reference image as an example.

...HyperGAN is currently beta. We are still searching for a default cross-data-set configuration. Each of the examples supports search. Automated search can help find good configurations. If you are unsure, you can start with the 2d-distribution.py. Check out random_search.py for possibilities, you'll likely want to modify it. The examples are capable of (sometimes) finding a good trainer, like 2d-distribution. Mixing and matching components seems to work.