Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both notions of similarity (for example, when two tops are interchangeable) and compatibility (items of possibly different type that can go together in an outfit). This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-toend model. To evaluate the learned representation, we crawled 68,306 outfits created by users on the Polyvore website. Our approach obtains 3-5% improvement over the state-of-the-art on outfit compatibility prediction and fill-in-the-blank tasks using our dataset, as well as an established smaller dataset, while supporting a variety of useful queries 1 .
We present Octopus, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace – the quality of work, total cost incurred, and time to completion. Previous control agents have mostly focused on cost-quality, or cost-time tradeoffs, but not on directly controlling all three in concert. A naive formulation of three-objective optimization is intractable; Octopus takes a hierarchical POMDP approach, with three different components responsible for setting the pay per task, selecting the next task, and controlling task-level quality. We demonstrate that Octopus significantly outperforms existing state-of-the-art approaches on real experiments. We also deploy Octopus on Amazon Mechanical Turk, showing its ability to manage tasks in a real-world, dynamic setting.
We present OCTOPUS, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace -the quality of work, total cost incurred, and time to completion. Previous control agents have mostly focused on cost-quality, or cost-time tradeoffs, but not on directly controlling all three in concert. A naive formulation of threeobjective optimization is intractable; OCTOPUS takes a hierarchical POMDP approach, with three different components responsible for setting the pay per task, selecting the next task, and controlling task-level quality. We demonstrate that OCTOPUS significantly outperforms existing state-of-the-art approaches on real experiments. We also deploy OCTOPUS on Amazon Mechanical Turk, showing its ability to manage tasks in a real-world, dynamic setting.
We revisit the fundamental problem of sorting objects using crowdsourced pairwise comparisons. Prior work either treats these comparisons as independent tasks-in which case the resulting judgments may end up being inconsistent, or fails to capture the accuracies of workers, or difficulties of the pairwise comparisons-in which case the resulting judgments may end up being consistent with each other, but ultimately more inaccurate. We adopt a holistic approach that constructs a graph across the set of objects respecting consistency constraints. Our key contribution is a novel method of encoding difficulty of comparisons in the form of constraints on edges. We couple that with an iterative E-M-style procedure to uncover information about latent variables and constraints, along with the graph structure. We show that our approach predicts edge directions as well as difficulty values more accurately than baselines on both real and simulated data, across graphs of various sizes.
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