Search Results for author: Viveka Kulharia

Found 8 papers, 3 papers with code

A Revised Generative Evaluation of Visual Dialogue

1 code implementation20 Apr 2020 Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr

Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge.

Calibrating Deep Neural Networks using Focal Loss

2 code implementations NeurIPS 2020 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania

To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function.

The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks

no code implementations25 Sep 2019 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania

When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.

Domain Partitioning Network

no code implementations21 Feb 2019 Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr

Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game.

Multi-Agent Diverse Generative Adversarial Networks

1 code implementation CVPR 2018 Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, Puneet K. Dokania

Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample.

Face Generation Image-to-Image Translation +1

Message Passing Multi-Agent GANs

no code implementations5 Dec 2016 Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri

As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs).

Image Generation

Contextual RNN-GANs for Abstract Reasoning Diagram Generation

no code implementations29 Sep 2016 Arnab Ghosh, Viveka Kulharia, Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal

Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence.

Generative Adversarial Network Video Generation

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