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Found 9 papers, 6 papers with code
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
Real-world datasets exhibit imbalances of varying types and degrees.
Improving GANs for Long-Tailed Data through Group Spectral Regularization
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.
Ranked #1 on
Image Generation
on iNaturalist 2019
Hierarchical Semantic Regularization of Latent Spaces in StyleGANs
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
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.
Ranked #3 on
Domain Adaptation
on Office-Home
S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
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.
Class Balancing GAN with a Classifier in the Loop
However, majority of the developments focus on performance of GANs on balanced datasets.
S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
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.
IIT (BHU) Submission for the ACL Shared Task on Named Entity Recognition on Code-switched Data
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).
NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets
This paper describes our participation in SemEval 2018 Task 3 on Irony Detection in Tweets.
