Join us this Thursday 11-24-2022, 15:30 PM UTC, for the ContinualAI Seminar, where Iuliia Pliushch (Goethe University) will present the paper:
Title: “When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics”
Abstract: Although a plethora of architectural variants for deep classification has been introduced over time, recent works have found empirical evidence towards similarities in their training process. It has been hypothesized that neural networks converge not only to similar representations, but also exhibit a notion of empirical agreement on which data instances are learned first. Following in the latter works′ footsteps, we define a metric to quantify the relationship between such classification agreement over time, and posit that the agreement phenomenon can be mapped to core statistics of the investigated dataset. We empirically corroborate this hypothesis across the CIFAR10, Pascal, ImageNet and KTH-TIPS2 datasets. Our findings indicate that agreement seems to be independent of specific architectures, training hyper-parameters or labels, albeit follows an ordering according to image statistics.
- YouTube link: https://www.youtube.com/watch?v=cuoGtsO1pKk
- Microsoft Teams: https://bit.ly/clai-seminars
- YouTube recordings of the previous sessions: https://www.youtube.com/c/ContinualAI
UTC - 15:30 PM
CET - 16:30 PM
PST - 07:30 AM
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Looking forward to seeing you all there!
All the best,
University of California