Search Results for author: Kowshik Thopalli

Found 8 papers, 1 papers with code

Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation

no code implementations5 Jan 2022 Kowshik Thopalli, Jayaraman J Thiagarajan, Rushil Anirudh, Pavan K Turaga

In contrast to existing adversarial training-based DA methods, our approach isolates feature learning and distribution alignment steps, and utilizes a primary-auxiliary optimization strategy to effectively balance the objectives of domain invariance and model fidelity.

Representation Learning Unsupervised Domain Adaptation

Improving Multi-Domain Generalization through Domain Re-labeling

no code implementations17 Dec 2021 Kowshik Thopalli, Sameeksha Katoch, Andreas Spanias, Pavan Turaga, Jayaraman J. Thiagarajan

In this paper, we focus on the challenging problem of multi-source zero-shot DG, where labeled training data from multiple source domains is available but with no access to data from the target domain.

Domain Generalization

Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration

no code implementations10 Feb 2020 Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri

The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generalizable models, has led to crucial insights into network initialization and has enabled efficient inferencing.

Transfer Learning

SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation

no code implementations11 Jun 2019 Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan Turaga

This paper represents a hybrid approach, where we assume simplified data geometry in the form of subspaces, and consider alignment as an auxiliary task to the primary task of maximizing performance on the source.

Auxiliary Learning Unsupervised Domain Adaptation

Perturbation Robust Representations of Topological Persistence Diagrams

1 code implementation ECCV 2018 Anirudh Som, Kowshik Thopalli, Karthikeyan Natesan Ramamurthy, Vinay Venkataraman, Ankita Shukla, Pavan Turaga

However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets.

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