Search Results for author:
Found 8 papers, 4 papers with code
Optimization and Optimizers for Adversarial Robustness
Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.
Optimization for Robustness Evaluation beyond $\ell_p$ Metrics
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems.
Early Stopping for Deep Image Prior
In this regard, the majority of DIP works for vision tasks only demonstrates the potential of the models -- reporting the peak performance against the ground truth, but provides no clue about how to operationally obtain near-peak performance without access to the groundtruth.
Self-Validation: Early Stopping for Single-Instance Deep Generative Priors
Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data.
Rethinking Transfer Learning for Medical Image Classification
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC).
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures
This Slide-to-Wall grasping task assumes no prior knowledge except the partial observation of a target object.
A Deep Learning Approach to Grasping the Invisible
The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning.
