no code implementations • 20 Feb 2024 • Anuj Kumar Sirohi, Anjali Gupta, Sayan Ranu, Sandeep Kumar, Amitabha Bagchi
Extensive experimentation on real-world datasets showcases the efficacy of GRAPHGINI in making significant improvements in individual fairness compared to all currently available state-of-the-art methods while maintaining utility and group equality.
no code implementations • 5 Feb 2024 • Hyoungseob Park, Anjali Gupta, Alex Wong
During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i. e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain.
1 code implementation • 7 May 2022 • Ashish Nair, Rahul Yadav, Anjali Gupta, Abhijnan Chakraborty, Sayan Ranu, Amitabha Bagchi
With the increasing popularity of food delivery platforms, it has become pertinent to look into the working conditions of the 'gig' workers in these platforms, especially providing them fair wages, reasonable working hours, and transparency on work availability.