activity
20192021
most citedFed-Focal Loss for imbalanced data classification in Federated Learning

34 citations · 39 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2021

One Shot Audio to Animated Video Generation

Neeraj Kumar, Srishti Goel, Ankur Narang +4

We consider the challenging problem of audio to animated video generation. We propose a novel method OneShotAu2AV to generate an animated video of arbitrary length using an audio c…

cs.CV2020

Robust One Shot Audio to Video Generation

Neeraj Kumar, Srishti Goel, Ankur Narang +1

Audio to Video generation is an interesting problem that has numerous applications across industry verticals including film making, multi-media, marketing, education and others. Hi…

cs.CV2020

Multi Modal Adaptive Normalization for Audio to Video Generation

Neeraj Kumar, Srishti Goel, Ankur Narang +1

Speech-driven facial video generation has been a complex problem due to its multi-modal aspects namely audio and video domain. The audio comprises lots of underlying features such…

eess.AS20205 cited

Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis

Neeraj Kumar, Srishti Goel, Ankur Narang +1

The style of the speech varies from person to person and every person exhibits his or her own style of speaking that is determined by the language, geography, culture and other fac…

cs.LG2020

CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning

Dipankar Sarkar, Sumit Rai, Ankur Narang

Federated learning has allowed the training of statistical models over remote devices without the transfer of raw client data. In practice, training in heterogeneous and large netw…

cs.LG202034 cited

Fed-Focal Loss for imbalanced data classification in Federated Learning

Dipankar Sarkar, Ankur Narang, Sumit Rai

The Federated Learning setting has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the da…