Search Results for author: Anirban Chakraborty

Found 25 papers, 5 papers with code

Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems

no code implementations5 May 2022 Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal, Himanshu Patil, Anirban Chakraborty

We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i. e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary.

Adversarial Attack Knowledge Distillation

MMD-ReID: A Simple but Effective Solution for Visible-Thermal Person ReID

1 code implementation9 Nov 2021 Chaitra Jambigi, Ruchit Rawal, Anirban Chakraborty

Learning modality invariant features is central to the problem of Visible-Thermal cross-modal Person Reidentification (VT-ReID), where query and gallery images come from different modalities.

Beyond Classification: Knowledge Distillation using Multi-Object Impressions

no code implementations27 Oct 2021 Gaurav Kumar Nayak, Monish Keswani, Sharan Seshadri, Anirban Chakraborty

Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student).

Classification Knowledge Distillation +2

Understanding the Galactic population of normal pulsars: A leap forward

1 code implementation24 Dec 2020 Anirban Chakraborty, Manjari Bagchi

By comparing the distributions of various parameters of synthetic pulsars detectable by the Parkes Multibeam Pulsar Survey, the Pulsar Arecibo L-band Feed Array Survey, and two Swinburne Multibeam surveys with those of the real pulsars detected by the same surveys, we find that a good and physically realistic model can be obtained by using a uniform distribution of the braking index in the range of 2. 5 to 3. 0, a uniform distribution of the cosine of the angle between the spin and the magnetic axis in the range of 0 to 1, a log-normal birth distribution of the surface magnetic field with the mean and the standard deviation as 12. 85 and 0. 55 respectively while keeping the distributions of other parameters unchanged from the ones most commonly used in the literature.

High Energy Astrophysical Phenomena Solar and Stellar Astrophysics

Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images

no code implementations29 Nov 2020 Shreya Roy, Anirban Chakraborty

We will dive into the recent evolution of the deep models in the context of SISR over the past few years and will present a comparative study between these models.

Image Super-Resolution Single Image Super Resolution

Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation

no code implementations18 Nov 2020 Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty

In such scenarios, existing approaches either iteratively compose a synthetic set representative of the original training dataset, one sample at a time or learn a generative model to compose such a transfer set.

Knowledge Distillation Transfer Learning

Kernel Density Estimation based Factored Relevance Model for Multi-Contextual Point-of-Interest Recommendation

no code implementations28 Jun 2020 Anirban Chakraborty, Debasis Ganguly, Annalina Caputo, Gareth J. F. Jones

An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized 'points-of-interest' (POIs) to a user, if it can extract information from the user's preference history (exploitation) and effectively blend it with the user's current contextual information (exploration) to predict a POI's 'appropriateness' in the current context.

Density Estimation

Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation

no code implementations24 Jun 2020 Jogendra Nath Kundu, Siddharth Seth, Rahul M. V, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty

However, generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable, as these models often perform unsatisfactorily on unseen in-the-wild environments.

3D Human Pose Estimation 3D Pose Estimation +2

Semantic Segmentation of highly class imbalanced fully labelled 3D volumetric biomedical images and unsupervised Domain Adaptation of the pre-trained Segmentation Network to segment another fully unlabelled Biomedical 3D Image stack

no code implementations13 Mar 2020 Shreya Roy, Anirban Chakraborty

We first perform Semantic Segmentation on the fully labeled isotropic biomedical source data (FIBSEM) and try to incorporate the the trained model for segmenting the target unlabelled dataset(SNEMI3D)which shares some similarities with the source dataset in the context of different types of cellular bodies and other cellular components.

Semantic Segmentation Unsupervised Domain Adaptation

DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier

no code implementations27 Dec 2019 Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, R. Venkatesh Babu

We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples from a trained classifier, using a novel Data-enriching GAN (DeGAN) framework.

Incremental Learning Knowledge Distillation +1

EvAn: Neuromorphic Event-based Anomaly Detection

no code implementations21 Nov 2019 Lakshmi Annamalai, Anirban Chakraborty, Chetan Singh Thakur

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events, thus resulting in significant advantages over conventional cameras in terms of low power utilization, high dynamic range, and no motion blur.

Anomaly Detection

Enhancing Salient Object Segmentation Through Attention

no code implementations27 May 2019 Anuj Pahuja, Avishek Majumder, Anirban Chakraborty, R. Venkatesh Babu

Segmenting salient objects in an image is an important vision task with ubiquitous applications.

Semantic Segmentation

Zero-Shot Knowledge Distillation in Deep Networks

1 code implementation20 May 2019 Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu, Anirban Chakraborty

Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation.

Knowledge Distillation

Adversarial Attacks and Defences: A Survey

no code implementations28 Sep 2018 Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, Debdeep Mukhopadhyay

Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past.

Operator-in-the-Loop Deep Sequential Multi-camera Feature Fusion for Person Re-identification

no code implementations19 Jul 2018 K L Navaneet, Ravi Kiran Sarvadevabhatla, Shashank Shekhar, R. Venkatesh Babu, Anirban Chakraborty

Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras.

Person Re-Identification

A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation

1 code implementation5 Jun 2014 Toufiq Parag, Anirban Chakraborty, Stephen Plaza, Lou Scheffer

In particular, given an over-segmented image or volume, we propose a novel framework for accurately clustering regions of the same neuron.


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