46 citations · 81 across the 9 of their papers we have counts for
9 papers
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi +2
Despite extensive research on training generative adversarial networks (GANs) with limited training data, learning to generate images from long-tailed training distributions remain…
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods
Mingqi Jiang, Saeed Khorram, Li Fuxin
In order to gain insights about the decision-making of different visual recognition backbones, we propose two methodologies, sub-explanation counting and cross-testing, that system…
Cycle-Consistent Counterfactuals by Latent Transformations
Saeed Khorram, Li Fuxin
CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either re…
From Heatmaps to Structural Explanations of Image Classifiers
Li Fuxin, Zhongang Qi, Saeed Khorram +4
This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained. The pape…
Stochastic Block-ADMM for Training Deep Networks
Saeed Khorram, Xiao Fu, Mohamad H. Danesh +2
In this paper, we propose Stochastic Block-ADMM as an approach to train deep neural networks in batch and online settings. Our method works by splitting neural networks into an arb…
Re-understanding Finite-State Representations of Recurrent Policy Networks
Mohamad H. Danesh, Anurag Koul, Alan Fern +1
We introduce an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy…
Sleep Stage Scoring Using Joint Frequency-Temporal and Unsupervised Features
Mohamadreza Jafaryani, Saeed Khorram, Vahid Pourahmadi +1
Patients with sleep disorders can better manage their lifestyle if they know about their special situations. Detection of such sleep disorders is usually possible by analyzing a nu…
Visualizing Deep Networks by Optimizing with Integrated Gradients
Zhongang Qi, Saeed Khorram, Li Fuxin
Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular appro…
Embedding Deep Networks into Visual Explanations
Zhongang Qi, Saeed Khorram, Fuxin Li
In this paper, we propose a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by learning a nonlinear embedding of a high-dime…