activity
20172020
most citedA Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

41 citations · 61 across the 5 of their papers we have counts for

collaborators

13 papers

cs.LG2020

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster +2

Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up…

cs.CV20193 cited

Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages

Nikolay Jetchev, Urs Bergmann, Gökhan Yildirim

Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material. Formally, this process takes a…

cs.LG2019

Set Flow: A Permutation Invariant Normalizing Flow

Kashif Rasul, Ingmar Schuster, Roland Vollgraf +1

We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data. As the architecture is an extension of RealNVPs, it inherits all i…

cs.CV2019

Generating High-Resolution Fashion Model Images Wearing Custom Outfits

Gökhan Yildirim, Nikolay Jetchev, Roland Vollgraf +1

Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determi…

cs.LG2019

A Hierarchical Bayesian Model for Size Recommendation in Fashion

Romain Guigourès, Yuen King Ho, Evgenii Koriagin +3

We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a cust…

cs.LG201941 cited

A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

Abdul-Saboor Sheikh, Romain Guigoures, Evgenii Koriagin +4

Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the bu…