Deep Image Prior

by Rehan Guha

Deep Image Prior defies the idea that “deep learning only works in the context of massive datasets or models pretrained on such datasets”. This paper showed that some deep neural networks could be successfully trained on a single image without large datasets, the structure of the network itself could be preventing deep networks from overfitting.


by Uttam Erukala et al

Screening through hundreds of mail and prioritizing your work for the day is a difficult job. One kind of prioritization that is important is dealing with deadlines that are indicated in emails such as a slide deck or report due. The first step of calendarizing deadlines is to identify that a deadline is indeed present. We’ve tried to tackle this deadline tagging as an email classification problem using a Bayesian approach with the result being PMail. We also built an RNN based model which we compare with the Bayesian approach. We were able to show promising results with the approaches we took, which we detail in this post.

Measuring feet using deep learning

by Pallavi Ramicetty et al

Approximate biometrics are often required for effective online shopping experience, for example, for clothing, eyewear and footwear. We experimented with Mask-RCNN based object segmentation for measuring human feet with the intention of recommending appropriate footwear, which we talk about in this post.

i-tagger - DL models for sequence tagging

by Mageswaran Dhandapani
i-tagger - Neural Networks based Deep Learning models and tools for sequence tagging. Developing models to solve a problem for a data set at hand, requires lot of trial and error methods. With current projects, we find a difficulty with supporting different datasets and models in a modular way. i-tagger helps with easing preprocessing, training and prediction.

Deep Type

by Manoj Kumar et al
At Imaginea, we run a social network for typoholics called Fontli as our designers have a passion for the field. Folks share typography that they catch in the wild or work that they’ve created themselves. Members ask others for font identification and tips, and tag what they’re able to identify themselves. Given that we’re into typography, we would love to have a system where we can take a picture of some type and apply it to text of our own choice!

Typography Detection

by Uttam Erukala et al

Keeping undesirable content out of social networks and communication channels is a common problem. Our email systems today have sophisticated “spam filters” thanks to which we’re protected from much harm and waste of time. The problem of spam is particularly harsh in niche social networks and interest groups which are small and sensitive to disruption. We run one such niche social network for typography enthusiasts called Fontli and we like to protect our dear typographers from content that they’re not interested in - which is everything that isn’t typography. The problem is that this is hard … even for humans!

In this post, we talk about a filter we recently developed and deployed to reduce and flag incidences of non-typographic content on Fontli, using a deep convolutional neural network based image classifier. We’ve had modest success and faced some intriguing situations and results along the way.