Search Results for author: Diganta Mukherjee

Found 7 papers, 0 papers with code

Significance of Anatomical Constraints in Virtual Try-On

no code implementations4 Jan 2024 Debapriya Roy, Sanchayan Santra, Diganta Mukherjee, Bhabatosh Chanda

In general, a VTON system takes a clothing source and a person's image to predict the try-on output of the person in the given clothing.

Anatomy Virtual Try-on

Significance of Skeleton-based Features in Virtual Try-On

no code implementations17 Aug 2022 Debapriya Roy, Sanchayan Santra, Diganta Mukherjee, Bhabatosh Chanda

The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an user the convenience of trying a clothing at the comfort of their home.

Virtual Try-on

Exploring Financial Networks Using Quantile Regression and Granger Causality

no code implementations21 Jul 2022 Kara Karpman, Samriddha Lahiry, Diganta Mukherjee, Sumanta Basu

We propose statistical methods that measure connectivity in the financial sector using system-wide tail-based analysis and is able to distinguish between connectivity in lower and upper tails of the return distribution.

regression Time Series Analysis

Multi-asset Generalised Variance Swaps in Barndorff-Nielsen and Shephard model

no code implementations26 Nov 2020 Subhojit Biswas, Diganta Mukherjee, Indranil SenGupta

This paper proposes swaps on two important new measures of generalized variance, namely the maximum eigenvalue and trace of the covariance matrix of the assets involved.

An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks

no code implementations30 Oct 2020 Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image.

Attribute Fairness

Discrete time portfolio optimisation managing value at risk under heavy tail return distribution

no code implementations11 Aug 2019 Subhojit Biswas, Diganta Mukherjee

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to the Value at Risk assuming a heavy tail distribution of the stock prices return.

Numerical Integration

Portfolio Optimization Managing Value at Risk under Heavy Tail Return, using Stochastic Maximum Principle

no code implementations11 Aug 2019 Subhojit Biswas, Mrinal K. Ghosh, Diganta Mukherjee

We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to managing the Value at Risk (VaR) assuming a heavy tailed distribution of the stock prices return.

Portfolio Optimization

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