4 citations · 4 across the 2 of their papers we have counts for
3 papers
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories
Fait Poms, Vishnu Sarukkai, Ravi Teja Mullapudi +4
For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistic…
Background Splitting: Finding Rare Classes in a Sea of Background
Ravi Teja Mullapudi, Fait Poms, William R. Mark +2
We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to t…
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Mayee F. Chen, Daniel Y. Fu, Frederic Sala +5
Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We…