no code implementations • CVPR 2023 • Arkanath Pathak, Nicholas Dufour
We iteratively train GAN-classifiers and train GANs that "fool" the classifiers (in an attempt to fill the knowledge gaps), and examine the effect on GAN training dynamics, output quality, and GAN-classifier generalization.
no code implementations • NeurIPS 2019 • Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee
Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time.
1 code implementation • 24 Aug 2017 • Xinchen Yan, Jasmine Hsu, Mohi Khansari, Yunfei Bai, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak Lee
Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations.
no code implementations • WS 2016 • Arkanath Pathak, Pawan Goyal, Plaban Bhowmick
We also propose two baselines and observe that the proposed approach outperforms baseline systems for the final task of Structure Prediction.