28 citations · 82 across the 15 of their papers we have counts for
6 papers · 1 filter
Simple Transferability Estimation for Regression Tasks
Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho +4
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which receive…
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
Cuong N. Nguyen, Lam Si Tung Ho, Vu Dinh +2
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predict…
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas, Meir Feder, Tal Hassner
Detecting out-of-distribution (OOD) samples is vital for developing machine learning based models for critical safety systems. Common approaches for OOD detection assume access to…
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V. Nguyen, Tal Hassner, Matthias Seeger +1
We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy…
Transferability and Hardness of Supervised Classification Tasks
Anh T. Tran, Cuong V. Nguyen, Tal Hassner
We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does n…
Toward Understanding Catastrophic Forgetting in Continual Learning
Cuong V. Nguyen, Alessandro Achille, Michael Lam +3
We study the relationship between catastrophic forgetting and properties of task sequences. In particular, given a sequence of tasks, we would like to understand which properties o…