33 citations · 33 across the 3 of their papers we have counts for
7 papers
On Duality Gap as a Measure for Monitoring GAN Training
Sahil Sidheekh, Aroof Aimen, Vineet Madan +1
Generative adversarial network (GAN) is among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging ta…
MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers
Rajat Sharma, Nikhil Reddy, Vidhya Kamakshi +2
The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-…
Wheat Crop Yield Prediction Using Deep LSTM Model
Sagarika Sharma, Sujit Rai, Narayanan C. Krishnan
An in-season early crop yield forecast before harvest can benefit the farmers to improve the production and enable various agencies to devise plans accordingly. We introduce a reli…
MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks
Ashish Kumar, Karan Sehgal, Prerna Garg +2
Deep convolutional networks have been quite successful at various image classification tasks. The current methods to explain the predictions of a pre-trained model rely on gradient…
Implicit Discriminator in Variational Autoencoder
Prateek Munjal, Akanksha Paul, Narayanan C. Krishnan
Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generati…
Semantically Aligned Bias Reducing Zero Shot Learning
Akanksha Paul, Narayanan C. Krishnan, Prateek Munjal
Zero shot learning (ZSL) aims to recognize unseen classes by exploiting semantic relationships between seen and unseen classes. Two major problems faced by ZSL algorithms are the h…