no code implementations • 2 Apr 2024 • Tatiana Gaintseva, Martin Benning, Gregory Slabaugh
Instead, based on CLIP embeddings of backlit and well-lit images from training data, we compute the residual vector in the embedding space as a simple difference between the mean embeddings of the well-lit and backlit images.
no code implementations • 14 Nov 2023 • Laida Kushnareva, Tatiana Gaintseva, German Magai, Serguei Barannikov, Dmitry Abulkhanov, Kristian Kuznetsov, Eduard Tulchinskii, Irina Piontkovskaya, Sergey Nikolenko
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated.
1 code implementation • 1 Dec 2019 • Maxim Borisyak, Tatiana Gaintseva, Andrey Ustyuzhanin
Adversarial Optimization (AO) provides a reliable, practical way to match two implicitly defined distributions, one of which is usually represented by a sample of real data, and the other is defined by a generator.