99 citations · 122 across the 9 of their papers we have counts for
9 papers
MADG: Margin-based Adversarial Learning for Domain Generalization
Aveen Dayal, Vimal K. B., Linga Reddy Cenkeramaddi +3
Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the…
Mitigating the Effect of Incidental Correlations on Part-based Learning
Gaurav Bhatt, Deepayan Das, Leonid Sigal +1
Intelligent systems possess a crucial characteristic of breaking complicated problems into smaller reusable components or parts and adjusting to new tasks using these part represen…
Explaining Deep Face Algorithms through Visualization: A Survey
Thrupthi Ann John, Vineeth N Balasubramanian, C. V. Jawahar
Although current deep models for face tasks surpass human performance on some benchmarks, we do not understand how they work. Thus, we cannot predict how it will react to novel inp…
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach
Vimal K B, Saketh Bachu, Tanmay Garg +3
Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years. Existing efforts…
Towards Estimating Transferability using Hard Subsets
Tarun Ram Menta, Surgan Jandial, Akash Patil +6
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suita…
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
Arjun Ashok, K J Joseph, Vineeth Balasubramanian
In class-incremental learning, the model is expected to learn new classes continually while maintaining knowledge on previous classes. The challenge here lies in preserving the mod…