Search Results for author:
Found 6 papers, 2 papers with code
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training.
HiP: Hierarchical Perceiver
This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations.
Training verified learners with learned verifiers
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i. e., networks that provably satisfy some desired input-output properties.
24/7 Place Recognition by View Synthesis
We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed.
All About VLAD
The objective of this paper is large scale object instance retrieval, given a query image.
