##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Modeling the effects of blurriness in mobile ads
The creative is the image of the ad that the user sees and engages with upon viewing. This talk studies the effect of an ad creative’s specifications and quality of render in performance campaigns and suggests a playbook for digital marketers based on the findings and insights. An offline study on an ad creative’s specifications such as resolution, aspect ratio, handset density, device orientation, etc. on fit (slot size x creative size) and quality of the render (blurriness / sharpness, etc.) to provide digital marketers with helpful insights.
Further, exploring the state of the art Super Resolution Generative Adversarial Networks for resolving the ad images so that even with an inventory of ad images that do not fit the slot sizes, these ad images can be super resolved and then served to improve on the performance as against the case where the ad images might be blurred out, otherwise.
Most often, an advertiser shares the same set of creatives with multiple ad networks and programmatic channels. These networks and DSPs then bid for the same impressions from a publisher with those same set of creatives. Needless to say, neither ad networks, nor advertisers benefit from this bidding war.
It’s clear from the above example that Media is a commodity; ad impressions can be bought from any media buying channel. The value being added by an ad network or DSP in this model is in matching demand with supply.
- What usually happens when it comes to rendering ad images?
- What we found out that is optimal in terms of performance?
- Sneak peek into the changes that can be performed to improve performance.
- SRGAN: Crash Course
- How we super resolve ad images.
Abhijith C. is a Data Scientist at InMobi Technologies with a prime area of focus on images. He’s a graduate from Sir. M. Visvesvaraya Institute of Technology, Bangalore (Computer Science). His area of interests are deep learning and computer vision. Abhijith has experience as a Machine Learning intern at Robert Bosch along with a stint at CDS, Indian Institute of Science. He is also an avid competitive coder. Football is his other love, and a Chelsea fanatic since a decade.