no code implementations • 11 Jan 2023 • Naveen Venkat, Mayank Agarwal, Maneesh Singh, Shubham Tulsiani
While this representation yields (coarsely) accurate images corresponding to novel viewpoints, the lack of geometric reasoning limits the quality of these outputs.
no code implementations • NeurIPS 2020 • Naveen Venkat, Jogendra Nath Kundu, Durgesh Kumar Singh, Ambareesh Revanur, R. Venkatesh Babu
Thus, we aim to utilize implicit alignment without additional training objectives to perform adaptation.
Domain Adaptation Multi-Source Unsupervised Domain Adaptation
no code implementations • ECCV 2020 • Jogendra Nath Kundu, Rahul Mysore Venkatesh, Naveen Venkat, Ambareesh Revanur, R. Venkatesh Babu
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA).
1 code implementation • CVPR 2020 • Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul M. V, R. Venkatesh Babu
Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future.
1 code implementation • CVPR 2020 • Jogendra Nath Kundu, Naveen Venkat, Rahul M. V, R. Venkatesh Babu
1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.