4 papers
The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning
Brent A. Griffin, Jason J. Corso
Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thres…
Zero-Shot Coreset Selection via Iterative Subspace Sampling
Brent A. Griffin, Jacob Marks, Jason J. Corso
Deep learning increasingly relies on massive data with substantial storage, annotation, and training costs. To reduce costs, coreset selection finds a representative subset of data…
Auto-Labeling Data for Object Detection
Brent A. Griffin, Manushree Gangwar, Jacob Sela +1
Great labels make great models. However, traditional labeling approaches for tasks like object detection have substantial costs at scale. Furthermore, alternatives to fully-supervi…
Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes
Jacob Marks, Brent A. Griffin, Jason J. Corso
We introduce a new framework for analyzing classification datasets based on the ratios of reconstruction errors between autoencoders trained on individual classes. This analysis fr…