Search Results for author: Anirban Chakraborty

Found 41 papers, 11 papers with code

Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage

1 code implementation28 Sep 2024 Anirban Chakraborty, Matthias Katzfuss

Many applications, including climate-model analysis and stochastic weather generators, require learning or emulating the distribution of a high-dimensional and non-Gaussian spatial field based on relatively few training samples.

Improving Domain Adaptation Through Class Aware Frequency Transformation

no code implementations28 Jul 2024 Vikash Kumar, Himanshu Patil, Rohit Lal, Anirban Chakraborty

Most of the Unsupervised Domain Adaptation (UDA) algorithms focus on reducing the global domain shift between labelled source and unlabelled target domains by matching the marginal distributions under a small domain gap assumption.

Pseudo Label Unsupervised Domain Adaptation

Scalable Model-Based Gaussian Process Clustering

no code implementations14 Sep 2023 Anirban Chakraborty, Abhisek Chakraborty

Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification.

Clustering Gaussian Processes +2

DAD++: Improved Data-free Test Time Adversarial Defense

2 code implementations10 Sep 2023 Gaurav Kumar Nayak, Inder Khatri, Shubham Randive, Ruchit Rawal, Anirban Chakraborty

With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios.

Adversarial Defense Adversarial Robustness +4

Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning

no code implementations31 May 2023 M. Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Simmhan, Anirban Chakraborty

In practice, there can often be substantial heterogeneity (e. g., class imbalance) across the local data distributions observed by each of these clients.

Federated Learning

Query-guided Attention in Vision Transformers for Localizing Objects Using a Single Sketch

no code implementations15 Mar 2023 Aditay Tripathi, Anand Mishra, Anirban Chakraborty

and Sketchy datasets, respectively, and a $12. 2\%$ improvement in AP@50 for large objects that are `unseen' during training.

Object object-detection +2

Edges to Shapes to Concepts: Adversarial Augmentation for Robust Vision

no code implementations CVPR 2023 Aditay Tripathi, Rishubh Singh, Anirban Chakraborty, Pradeep Shenoy

We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures.

Classification Data Augmentation

Robustifying Deep Vision Models Through Shape Sensitization

no code implementations14 Nov 2022 Aditay Tripathi, Rishubh Singh, Anirban Chakraborty, Pradeep Shenoy

We also obtain gains of up to 28% and 8. 5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViT-B) and ImageNet-R (for ViT-S), respectively.

Classification Data Augmentation

CoNMix for Source-free Single and Multi-target Domain Adaptation

1 code implementation7 Nov 2022 Vikash Kumar, Rohit Lal, Himanshu Patil, Anirban Chakraborty

The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing.

Domain Adaptation Knowledge Distillation +2

Robust Few-shot Learning Without Using any Adversarial Samples

1 code implementation3 Nov 2022 Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty

These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden.

Decision Making Few-Shot Learning

Data-free Defense of Black Box Models Against Adversarial Attacks

1 code implementation3 Nov 2022 Gaurav Kumar Nayak, Inder Khatri, Ruchit Rawal, Anirban Chakraborty

At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy.

Adversarial Robustness

Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing

no code implementations3 Nov 2022 Aditay Tripathi, Anand Mishra, Anirban Chakraborty

In VL-MPAG Net, we first construct a directed graph with object proposals as nodes and an edge between a pair of nodes representing a plausible relation between them.

Graph Neural Network Object +1

DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers

no code implementations17 Oct 2022 Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty

Existing works use this technique to provably secure a pretrained non-robust model by training a custom denoiser network on entire training data.

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 +3

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

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.

Data-free 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.

Segmentation Semantic Segmentation +1

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.

Data-free Knowledge Distillation Incremental Learning +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 Generative Adversarial Network +1

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.

Object Segmentation +1

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.

Deep Learning Survey

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.

Clustering Segmentation +1

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