What is DECILE

Data Efficient Learning with Less Data
State of the art AI and Deep Learning are very data hungry. This comes at significant cost including larger resource costs (multiple expensive GPUs and cloud costs), training times (often times multiple days), and human labeling costs and time. Decile attempts to solve this by answering the following question. Can we train state of the art deep models with only a sample (say 5 to 10\%) of massive datasets, while having neglibible impact in accuracy? Can we do this while reducing training time/cost by an order of magnitude, and/or significantly reducing the amount of labeled data required?

Need for DECILE

Staggering Training Costs of Deep Learning

Labeling Large Datasets is Expensive

Noise and Imbalance in Data

Human Consumption and Data Overload

Modules

Reduce end to end training time from days to hours and hours to minutes using coresets and data selection. CORDS implements a number of state of the art data subset selection algorithms and coreset algorithms. Some of the algorithms currently implemented with CORDS include: GLISTER, GradMatchOMP, GradMatchFixed, CRAIG, SubmodularSelection, RandomSelection etc

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DISTIL is a library that features many state-of-the-art active learning algorithms. Implemented in PyTorch, it gives fast and efficient implementations of these active learning algorithms. It allows users to modularly insert active learning selection into their pre-existing training loops with minimal change. Most importantly, it features promising results in achieving high model performance with less amount of labeled data. If you are looking to cut down on labeling costs, DISTIL should be your go-to for getting the most out of your data.

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Summarize massive datasets using submodular optimization

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SPEAR is a python library that reduce data labeling efforts using data programming. It implements several recent approaches such as Snorkel, ImplyLoss, Learning to reweight, etc. In addition to data labeling, it integrates semi-supervised approaches for training and inference.

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Targeted subset selection

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News

Decile integrated into modern healthcare curriculum

MUMBAI: When the Indian Institute of Technology, Bombay (IITB), opens its gates, even if virtually, to the fresh batch of students this August, healthcare informatics will be one of its new offerings. The institute’s foray into healthcare will be a coming together of core sciences—maths, medicine and computer science. It will be offered as an interdisciplinary dual degree programme as well as a minor programme.
IIT-Bombay utilizes Decile technology to monitor suspicious movements

MUMBAI: Surakshavyuh makes surveillance smart and does in real-time what is usually a human’s job — monitoring CCTV camera footage for hours and alerting on suspicious movements
Decile used to develop Artificial Intelligence in CCTV software

MUMBAI: Spotting every biker who travelled without a helmet in Mumbai in the last week may require the police control rooms to go through terabytes of video footage. But now, that data can be extracted in a short span of time not by deploying Israeli tech, but by using local solutions developed by IIT-Bombay.
Military surveillance utilizes Decile for remote monitoring

MUMBAI: A state-of-the-art video surveillance platform developed by researchers at the Indian Institute of Technology-Bombay (IIT-B) has found application in military surveillance as well as remote monitoring of social distancing norms violations amid Covid-19 pandemic.