Proposed 'Dualing GANs', reformulating the GAN min-max objective into a minimization problem using duality, revealing connections between GANs and moment matching.
Developed 'Sliced Wasserstein GAN', removing the Kantorovich-Rubinstein duality via one-dimensional projections.
Introduced 'Max-Sliced Distance' to reduce computational cost and improve training stability.
Investigated challenges in backpropagating through GANs to latent space for image inpainting, proposing solutions using annealed importance sampling with Hamiltonian Monte Carlo.
Created and released the SAILVOS dataset for amodal instance-level video object segmentation.
Released open-source code for multiple projects, including Sliced Wasserstein GAN and backpropagation tools.