Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used....Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used.Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint.In that paper,we present a novel visual analytics approach to interactively prune those networks and thus limit that issue.Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable.We assess the well-founded of our approach through two realistic case studies and a user study.For both of them,the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.展开更多
The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data.In these representations,data are encoded with various visual attributes depending on the need...The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data.In these representations,data are encoded with various visual attributes depending on the needs of the representation itself.To make coherent design choices about visual attributes,the visual search field proposes guidelines based on the human brain’s perception of features.However,information visualization representations frequently need to depict more data than the amount these guidelines have been validated on.Since,the information visualization community has extended these guidelines to a wider parameter space.This paper contributes to this theme by extending visual search theories to an information visualization context.We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors.Stimuli are defined by color and shape features for the purpose of visually encoding categorical data.The experimental protocol is made of a parameters space reduction step(i.e.,sub-sampling)based on a machine learning model,and a user evaluation to validate hypotheses and measure capacity limits.The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier.When redundantly encoded,the display heterogeneity has no effect on the task.When encoded with one attribute,the difficulty depends on that attribute heterogeneity until its capacity limit(7 for color,5 for shape)is reached.Finally,when encoded with two attributes simultaneously,performances drop drastically even with minor heterogeneity.展开更多
基金We acknowledge the Nouvelle-Aquitaine Region,Bordeaux Métropole and SUEZ,le LyRE for mainly funding and supporting this work through the Convention N°AAPR2020-2019-8171810。
文摘Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used.Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint.In that paper,we present a novel visual analytics approach to interactively prune those networks and thus limit that issue.Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable.We assess the well-founded of our approach through two realistic case studies and a user study.For both of them,the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.
文摘The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data.In these representations,data are encoded with various visual attributes depending on the needs of the representation itself.To make coherent design choices about visual attributes,the visual search field proposes guidelines based on the human brain’s perception of features.However,information visualization representations frequently need to depict more data than the amount these guidelines have been validated on.Since,the information visualization community has extended these guidelines to a wider parameter space.This paper contributes to this theme by extending visual search theories to an information visualization context.We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors.Stimuli are defined by color and shape features for the purpose of visually encoding categorical data.The experimental protocol is made of a parameters space reduction step(i.e.,sub-sampling)based on a machine learning model,and a user evaluation to validate hypotheses and measure capacity limits.The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier.When redundantly encoded,the display heterogeneity has no effect on the task.When encoded with one attribute,the difficulty depends on that attribute heterogeneity until its capacity limit(7 for color,5 for shape)is reached.Finally,when encoded with two attributes simultaneously,performances drop drastically even with minor heterogeneity.
