1.5k citations · 2.1k across the 26 of their papers we have counts for
14 papers · 1 filter
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Giang, Nguyen, Valerie Chen +2
Nearest neighbors (NN) are traditionally used to compute final decisions, e.g., in Support Vector Machines or k-NN classifiers, and to provide users with explanations for the model…
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
Ronast Subedi, Rebati Raman Gaire, Sharib Ali +3
This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belo…
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification
Mohammad Reza Taesiri, Giang Nguyen, Sarra Habchi +2
Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers to…
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Giang Nguyen, Mohammad Reza Taesiri, Anh Nguyen
Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In…
How explainable are adversarially-robust CNNs?
Mehdi Nourelahi, Lars Kotthoff, Peijie Chen +1
Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria…
DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification
Hai Phan, Anh Nguyen
Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement. State-of-the-art FI approaches compare two images by taking the cosine similari…
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, Anh Nguyen
Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. Hundreds of papers have either proposed new…
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
Peijie Chen, Chirag Agarwal, Anh Nguyen
Increasingly more similarities between human vision and convolutional neural networks (CNNs) have been revealed in the past few years. Yet, vanilla CNNs often fall short in general…
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Michael A. Alcorn, Qi Li, Zhitao Gong +4
Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial on…
VectorDefense: Vectorization as a Defense to Adversarial Examples
Vishaal Munusamy Kabilan, Brandon Morris, Anh Nguyen
Training deep neural networks on images represented as grids of pixels has brought to light an interesting phenomenon known as adversarial examples. Inspired by how humans reconstr…
Spatial PixelCNN: Generating Images from Patches
Nader Akoury, Anh Nguyen
In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global fe…
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Mohammed Sadegh Norouzzadeh, Anh Nguyen, Margaret Kosmala +4
Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would revolutionize our ability to study and conserve ecosystems. We in…
Understanding Neural Networks Through Deep Visualization
Jason Yosinski, Jeff Clune, Anh Nguyen +2
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recogn…
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Nguyen, Jason Yosinski, Jeff Clune
Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given…