Search Results for author: Harsh Rangwani

Found 9 papers, 6 papers with code

Improving GANs for Long-Tailed Data through Group Spectral Regularization

1 code implementation21 Aug 2022 Harsh Rangwani, Naman Jaswani, Tejan Karmali, Varun Jampani, R. Venkatesh Babu

Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples.

Conditional Image Generation

Hierarchical Semantic Regularization of Latent Spaces in StyleGANs

no code implementations7 Aug 2022 Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh Singh, R. Venkatesh Babu

The quality of the generated images is predicated on two assumptions; (a) The richness of the hierarchical representations learnt by the generator, and, (b) The linearity and smoothness of the style spaces.

A Closer Look at Smoothness in Domain Adversarial Training

1 code implementation16 Jun 2022 Harsh Rangwani, Sumukh K Aithal, Mayank Mishra, Arihant Jain, R. Venkatesh Babu

Based on the analysis, we introduce the Smooth Domain Adversarial Training (SDAT) procedure, which effectively enhances the performance of existing domain adversarial methods for both classification and object detection tasks.

Domain Adaptation Object Detection

S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation

1 code implementation18 Sep 2021 Harsh Rangwani, Arihant Jain, Sumukh K Aithal, R. Venkatesh Babu

Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain.

Unsupervised Domain Adaptation

Class Balancing GAN with a Classifier in the Loop

1 code implementation17 Jun 2021 Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu

However, majority of the developments focus on performance of GANs on balanced datasets.

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation

1 code implementation ICCV 2021 Harsh Rangwani, Arihant Jain, Sumukh K Aithal, R. Venkatesh Babu

Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain.

Unsupervised Domain Adaptation

IIT (BHU) Submission for the ACL Shared Task on Named Entity Recognition on Code-switched Data

no code implementations WS 2018 Shashwat Trivedi, Harsh Rangwani, Anil Kumar Singh

This paper describes the best performing system for the shared task on Named Entity Recognition (NER) on code-switched data for the language pair Spanish-English (ENG-SPA).

named-entity-recognition NER

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