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.
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).
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 • 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.