We strongly believe in open source and giving to our community. We work directly with researchers in academia and seek out new perspectives with our intern and fellowship programs. We generalize our solutions and release them to the world as open source projects. We host discussions and publish our results.


ICIP, September, 2015

Mine the Fine: Fine-Grained Fragment Discovery

M. Hadi Kiapour, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu

While discriminative visual element mining has been introduced before, in this paper we present an approach that requires minimal annotation in both training and test time. Given only a bounding box localization of the foreground objects, our approach automatically transforms the input images into a roughly-aligned pose space and discovers the most discriminative visual fragments for each category.

These fragments are then used to learn robust classifiers that discriminate between very similar categories under challenging conditions such as large variations in pose or habitats. The minimal required input, is a critical characteristic that enables our approach to generalize over visual domains where expert knowledge is not readily available.

Moreover, our approach takes advantage of deep networks that are targeted towards fine-grained classification.It learns mid-level representations that are specific to a category and generalize well across the category instances at the same time.

Our evaluations demonstrate that the automatically learned representation based on discriminative fragments, significantly outperforms globally extracted deep features in classification accuracy.

2015 International Conference for Machine Learning (ICML)

Bayesian and Empirical Bayesian Forests

Matt Taddy, Chun-Sheng Chen, Jun Yu, Mitch Wyle

We derive ensembles of decision trees through a nonparametric Bayesian model, allowing us to view random forests as samples from a posterior distribution. This insight provides large gains in interpretability, and motivates a class of Bayesian forest (BF) algorithms that yield small but reliable performance gains.

Based on the BF framework, we are able to show that high-level tree hierarchy is stable in large samples. This leads to an empirical Bayesian forest (EBF) algorithm for building approximate BFs on massive distributed datasets and we show that EBFs outperform subsampling based alternatives by a large margin.