1 code implementation • CVPR 2023 • Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris Metaxas, Jian Ren
We propose a novel model-based guidance built upon the classifier-free guidance so that the knowledge from the model trained on a single image can be distilled into the pre-trained diffusion model, enabling content creation even with one given image.
no code implementations • NeurIPS 2020 • Arnab Ghosh, Harkirat Behl, Emilien Dupont, Philip Torr, Vinay Namboodiri
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
no code implementations • 18 Jun 2020 • Arnab Ghosh, Harkirat Singh Behl, Emilien Dupont, Philip H. S. Torr, Vinay Namboodiri
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
1 code implementation • ICCV 2019 • Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman
We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects.
no code implementations • 17 Apr 2018 • Rodrigo de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip Torr
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
1 code implementation • CVPR 2018 • Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, Puneet K. Dokania
Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample.
no code implementations • 5 Dec 2016 • Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri
As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs).
no code implementations • 29 Sep 2016 • Arnab Ghosh, Viveka Kulharia, Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal
Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence.