2 papers
cs.CV2023
Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis
Mohammad Mahdi Mehmanchi, Mahbod Nouri, Mohammad Sabokrou
This paper proposes a straightforward and cost-effective approach to assess whether a deep neural network (DNN) relies on the primary concepts of training samples or simply learns…
cs.LG2022
Joint Manifold Learning and Density Estimation Using Normalizing Flows
Seyedeh Fatemeh Razavi, Mohammad Mahdi Mehmanchi, Reshad Hosseini +1
Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding t…