Myths and Realities of Data Labeling for Deep Learning
In this BoF, we will explore data labeling tasks for NLP and CV problems. Specifically, we will discusses nuiances around defining, crowd sourcing and executing data labeling tasks, along with quality assurance processes. We shall also discuss machine aided data taggint to save cost, time and efforts on different data labeling tasks. Finally, we shall also touch upon feedback loopswhen some of the unseen and real-time inputs are labeled to fine-tune the deep learning models.
- setting the context : data labeling for NLP and CV
- how to define a data labeling task : novice vs expert
- does crowd sourcing of data labeling really work : adv vs disadv.
- how to manage in house data labeling teams : adv vs disadv
- what is the criticality of the correctness of data labels
- what is the experience and expertise expectation of data labelers
- how to ensure correctness of data labels : manual vs automated checks
- how to resolve labeling conflicts
- how does an engineer know if she has enough labeled data
- what are the time, cost, correctness trade-offs
- how to ensure and execute class balanced data labeling
- how to plan and execute weakly supervised data labeling
- how to train models on small set of labeled data and generate ‘soft tags’ for the rest of the unlabeled data
- how does one know if a model is performing well in practice on unseen and real-time inputs
- how does feedback loop work when some of the unseen and real-time inputs are labeled to fine-tune the models
Familiarity with NLP, CV, Deep Learning
Vijay is the co-founder and CTO of Infilect Technologies, a Computer Vision and Deep Learning start-up, builidng B2B SaaS products for global retail industry. Vijay has a PhD in CSE, from IIT Bombay. Vijay has worked as research scientist in IBM Research Labs.