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Found 19 papers, 7 papers with code
Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement
In this paper, we introduce a lightweight, user-friendly, and scalable framework that synergizes human and machine intelligence for efficient dataset validation and quality enhancement.
Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs
Given that a conventional laparoscope only provides a two-dimensional (2-D) view, the detection and diagnosis of medical ailments can be challenging.
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
In this survey, we review a plethora of research efforts conducted on image-oriented SSL, providing a historic view and paying attention to best practices as well as useful software packages.
A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets
Deep Neural Network (DNN) models are increasingly evaluated using new replication test datasets, which have been carefully created to be similar to older and popular benchmark datasets.
Exact Feature Collisions in Neural Networks
Predictions made by deep neural networks were shown to be highly sensitive to small changes made in the input space where such maliciously crafted data points containing small perturbations are being referred to as adversarial examples.
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
We find that $71\%$ of the adversarial examples that achieve model-to-model adversarial transferability are misclassified into one of the top-5 classes predicted for the underlying source images.
Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
Although the adoption rate of deep neural networks (DNNs) has tremendously increased in recent years, a solution for their vulnerability against adversarial examples has not yet been found.
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Another concern that is often cited when designing smart home applications is the resilience of these applications against cyberattacks.
Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks.
Perturbation Analysis of Gradient-based Adversarial Attacks
Our experiments reveal that the Iterative Fast Gradient Sign attack, which is thought to be fast for generating adversarial examples, is the worst attack in terms of the number of iterations required to create adversarial examples in the setting of equal perturbation.
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models.
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning.
Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning
Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment.
How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
Nowadays, the output of the softmax function is also commonly used to assess the strength of adversarial examples: malicious data points designed to fail machine learning models during the testing phase.
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations.
Interpretable Convolutional Neural Networks for Effective Translation Initiation Site Prediction
Thanks to rapidly evolving sequencing techniques, the amount of genomic data at our disposal is growing increasingly large.
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks
A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) (Bradbury et al. (2017)).
Improving Language Modeling using Densely Connected Recurrent Neural Networks
In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks.
