indian institute of science

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Indian Institute of Science is a public institute and deemed university for research and higher education in science and engineering, located in Bangalore in the Indian state of Karnataka.

Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware

Every day around the world, interminable terabytes of data are being cap...
12/18/2019 ∙ by Yeshwanth Ravi Theja Bethi, et al. ∙ 53 ∙ share

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CUDA: Contradistinguisher for Unsupervised Domain Adaptation

In this paper, we propose a simple model referred as Contradistinguisher...
09/08/2019 ∙ by Sourabh Balgi, et al. ∙ 28 ∙ share

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EvAn: Neuromorphic Event-based Anomaly Detection

Event-based cameras are bio-inspired novel sensors that asynchronously r...
11/21/2019 ∙ by Lakshmi Annamalai, et al. ∙ 26 ∙ share

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GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions

Despite the remarkable success of generative adversarial networks, their...
08/11/2019 ∙ by Jogendra Nath Kundu, et al. ∙ 21 ∙ share

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Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images

In the emerging commercial space industry there is a drastic increase in...
08/03/2020 ∙ by Gaurav Kumar Nayak, et al. ∙ 20 ∙ share

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DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder

Accurate reconstruction of static environments from LiDAR scans of scene...
05/26/2021 ∙ by Prashant Kumar, et al. ∙ 19 ∙ share

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Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Existing fast algorithms for bilateral and nonlocal means filtering most...
11/06/2018 ∙ by Pravin Nair, et al. ∙ 18 ∙ share

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MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

Developing accurate, transferable and computationally inexpensive machin...
11/14/2018 ∙ by Soumya Sanyal, et al. ∙ 12 ∙ share

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Online Lifelong Generalized Zero-Shot Learning

Methods proposed in the literature for zero-shot learning (ZSL) are typi...
03/19/2021 ∙ by Chandan Gautam, et al. ∙ 12 ∙ share

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The role of surrogate models in the development of digital twins of dynamic systems

Digital twin technology has significant promise, relevance and potential...
01/25/2020 ∙ by Souvik Chakraborty, et al. ∙ 11 ∙ share

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Two Tier Prediction of Stroke Using Artificial Neural Networks and Support Vector Machines

Cerebrovascular accident (CVA) or stroke is the rapid loss of brain func...
03/17/2020 ∙ by Jerrin Thomas Panachakel, et al. ∙ 10 ∙ share

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Quantization-Aware Phase Retrieval

We address the problem of phase retrieval (PR) from quantized measuremen...
10/02/2018 ∙ by Subhadip Mukherjee, et al. ∙ 8 ∙ share

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A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19

The outbreak of the novel coronavirus (COVID-19) is unfolding as a major...
07/25/2020 ∙ by Rahul Singh, et al. ∙ 8 ∙ share

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Binary Document Image Super Resolution for Improved Readability and OCR Performance

There is a need for information retrieval from large collections of low-...
12/06/2018 ∙ by Ram Krishna Pandey, et al. ∙ 6 ∙ share

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Learning to Count in the Crowd from Limited Labeled Data

Recent crowd counting approaches have achieved excellent performance. ho...
07/07/2020 ∙ by Vishwanath A. Sindagi, et al. ∙ 6 ∙ share

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TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection

Visual steel surface defect detection is an essential step in steel shee...
01/18/2021 ∙ by Praveen Damacharla, et al. ∙ 6 ∙ share

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One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks

A robot that can operate in novel and unstructured environments must be ...
12/26/2020 ∙ by Sagar Gubbi Venkatesh, et al. ∙ 5 ∙ share

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Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions

Deep learning models are susceptible to input specific noise, called adv...
08/03/2018 ∙ by Konda Reddy Mopuri, et al. ∙ 4 ∙ share

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Fast Adaptive Bilateral Filtering

In the classical bilateral filter, a fixed Gaussian range kernel is used...
11/06/2018 ∙ by Ruturaj G. Gavaskar, et al. ∙ 4 ∙ share

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UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation

Aiming towards human-level generalization, there is a need to explore ad...
08/11/2019 ∙ by Jogendra Nath Kundu, et al. ∙ 4 ∙ share

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Teaching Robots Novel Objects by Pointing at Them

Robots that must operate in novel environments and collaborate with huma...
12/25/2020 ∙ by Sagar Gubbi Venkatesh, et al. ∙ 3 ∙ share

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Mango Tree Net -- A fully convolutional network for semantic segmentation and individual crown detection of mango trees

This work presents a method for semantic segmentation of mango trees in ...
07/16/2019 ∙ by Vikas Agaradahalli Gurumurthy, et al. ∙ 2 ∙ share

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Contradistinguisher: Applying Vapnik's Philosophy to Unsupervised Domain Adaptation

A complex combination of simultaneous supervised-unsupervised learning i...
05/25/2020 ∙ by Sourabh Balgi, et al. ∙ 2 ∙ share

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Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

With the research into development of quadruped robots picking up pace, ...
07/28/2020 ∙ by Sashank Tirumala, et al. ∙ 2 ∙ share

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Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection

We introduce a detection framework for dense crowd counting and eliminat...
06/18/2019 ∙ by Deepak Babu Sam, et al. ∙ 1 ∙ share

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A Re-evaluation of Knowledge Graph Completion Methods

Knowledge Graph Completion (KGC) aims at automatically predicting missin...
11/10/2019 ∙ by Zhiqing Sun, et al. ∙ 1 ∙ share

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A Naturalness Evaluation Database for Video Prediction Models

The study of video prediction models is believed to be a fundamental app...
05/01/2020 ∙ by Nagabhushan Somraj, et al. ∙ 1 ∙ share

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A Dual Framework for Low-rank Tensor Completion

We propose a novel formulation of the low-rank tensor completion problem...
12/04/2017 ∙ by Madhav Nimishakavi, et al. ∙ 0 ∙ share

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Groupwise Maximin Fair Allocation of Indivisible Goods

We study the problem of allocating indivisible goods among n agents in a...
11/21/2017 ∙ by Siddharth Barman, et al. ∙ 0 ∙ share

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A unified decision making framework for supply and demand management in microgrid networks

This paper considers two important problems - on the supply-side and dem...
11/14/2017 ∙ by Raghuram Bharadwaj Diddigi, et al. ∙ 0 ∙ share

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Transfer Learning in CNNs Using Filter-Trees

Convolutional Neural Networks (CNNs) are very effective for many pattern...
11/27/2017 ∙ by Suresh Kirthi Kumaraswamy, et al. ∙ 0 ∙ share

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Artifact reduction for separable non-local means

It was recently demonstrated [J. Electron. Imaging, 25(2), 2016] that on...
10/26/2017 ∙ by Sanjay Ghosh, et al. ∙ 0 ∙ share

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Generalized Dropout

Deep Neural Networks often require good regularizers to generalize well....
11/21/2016 ∙ by Suraj Srinivas, et al. ∙ 0 ∙ share

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Conditions for Stability and Convergence of Set-Valued Stochastic Approximations: Applications to Approximate Value and Fixed point Iterations

The main aim of this paper is the development of easily verifiable suffi...
09/14/2017 ∙ by Arunselvan Ramaswamy, et al. ∙ 0 ∙ share

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SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks

The ability to semantically interpret hand-drawn line sketches, although...
09/05/2017 ∙ by Ravi Kiran Sarvadevabhatla, et al. ∙ 0 ∙ share

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CNN Fixations: An unraveling approach to visualize the discriminative image regions

Deep convolutional neural networks (CNN) have revolutionized various fie...
08/22/2017 ∙ by Konda Reddy Mopuri, et al. ∙ 0 ∙ share

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Switching Convolutional Neural Network for Crowd Counting

We propose a novel crowd counting model that maps a given crowd scene to...
08/01/2017 ∙ by Deepak Babu Sam, et al. ∙ 0 ∙ share

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Confidence estimation in Deep Neural networks via density modelling

State-of-the-art Deep Neural Networks can be easily fooled into providin...
07/21/2017 ∙ by Akshayvarun Subramanya, et al. ∙ 0 ∙ share

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DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data

A class of recent approaches for generating images, called Generative Ad...
06/07/2017 ∙ by Swaminathan Gurumurthy, et al. ∙ 0 ∙ share

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Deep image representations using caption generators

Deep learning exploits large volumes of labeled data to learn powerful m...
05/25/2017 ∙ by Konda Reddy Mopuri, et al. ∙ 0 ∙ share

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Amortized Inference and Learning in Latent Conditional Random Fields for Weakly-Supervised Semantic Image Segmentation

Conditional random fields (CRFs) are commonly employed as a post-process...
05/03/2017 ∙ by Gaurav Pandey, et al. ∙ 0 ∙ share

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Analysis of gradient descent methods with non-diminishing, bounded errors

The main aim of this paper is to provide an analysis of gradient descent...
04/01/2016 ∙ by Arunselvan Ramaswamy, et al. ∙ 0 ∙ share

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Training Sparse Neural Networks

Deep neural networks with lots of parameters are typically used for larg...
11/21/2016 ∙ by Suraj Srinivas, et al. ∙ 0 ∙ share

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Compensating for Large In-Plane Rotations in Natural Images

Rotation invariance has been studied in the computer vision community pr...
11/17/2016 ∙ by Lokesh Boominathan, et al. ∙ 0 ∙ share

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Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition

Freehand sketching is an inherently sequential process. Yet, most approa...
08/11/2016 ∙ by Ravi Kiran Sarvadevabhatla, et al. ∙ 0 ∙ share

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SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition

Current state of the art object recognition architectures achieve impres...
07/29/2016 ∙ by Ravi Kiran Sarvadevabhatla, et al. ∙ 0 ∙ share

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Inducing Interpretability in Knowledge Graph Embeddings

We study the problem of inducing interpretability in KG embeddings. Spec...
12/10/2017 ∙ by Chandrahas, et al. ∙ 0 ∙ share

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CANDiS: Coupled & Attention-Driven Neural Distant Supervision

Distant Supervision for Relation Extraction uses heuristically aligned t...
10/26/2017 ∙ by Tushar Nagarajan, et al. ∙ 0 ∙ share

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Event Schema Induction using Tensor Factorization with Back-off

The goal of Event Schema Induction(ESI) is to identify schemas of events...
07/06/2017 ∙ by Madhav Nimishakavi, et al. ∙ 0 ∙ share

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Relation Schema Induction using Tensor Factorization with Side Information

Given a set of documents from a specific domain (e.g., medical research ...
05/12/2016 ∙ by Madhav Nimishakavi, et al. ∙ 0 ∙ share

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