Search Results for author:
Found 17 papers, 3 papers with code
Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility
This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision.
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model.
Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi Measures
Designing machine learning algorithms that are accurate yet fair, not discriminating based on any sensitive attribute, is of paramount importance for society to accept AI for critical applications.
Renyi Fair Information Bottleneck for Image Classification
We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB).
Robustness and Adaptation to Hidden Factors of Variation
We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data.
EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation
Skin lesions can be an early indicator of a wide range of infectious and other diseases.
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
To this end, several contributions are made here: A) we develop a new metric called mean Attack Success over Transformations (mAST) to evaluate patch attack robustness and invariance; and B), we systematically assess robustness of patch attacks to 3D position and orientation for various conditions; in particular, we conduct a sensitivity analysis which provides important qualitative insights into attack effectiveness as a function of the 3D pose of a patch relative to the camera (rotation, translation) and sets forth some properties for patch attack 3D invariance; and C), we draw novel qualitative conclusions including: 1) we demonstrate that for some 3D transformations, namely rotation and loom, increasing the training distribution support yields an increase in patch success over the full range at test time.
Adaptation and Generalization for Unknown Sensitive Factors of Variations
We also demonstrate how adaptation to real factors of variations can be performed in the semi-supervised case where some target factor labels are known, via automated intervention selection.
Practical Blind Membership Inference Attack via Differential Comparisons
The success of the former heavily depends on the quality of the shadow model, i. e., the transferability between the shadow and the target; the latter, given only blackbox probing access to the target model, cannot make an effective inference of unknowns, compared with MI attacks using shadow models, due to the insufficient number of qualified samples labeled with ground truth membership information.
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis
We present a technique that utilizes properties of random projections to characterize the behavior of clean and adversarial examples across a diverse set of subspaces.
Addressing Visual Search in Open and Closed Set Settings
We interpret both detection problems through a probabilistic, Bayesian lens, whereby the objectness maps produced by our method serve as priors in a maximum-a-posteriori approach to the detection step.
Least $k$th-Order and Rényi Generative Adversarial Networks
Another novel GAN generator loss function is next proposed in terms of R\'{e}nyi cross-entropy functionals with order $\alpha >0$, $\alpha\neq 1$.
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches.
Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics
Using novel generative methods for addressing missing subpopulation training data (DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of 72. 0% (65. 8%, 78. 2%), and for darker-skin, of 71. 5% (65. 2%, 77. 8%), demonstrating closer parity (delta=0. 5%) in accuracy across subpopulations (Welch t-test t=0. 111, P=. 912).
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection
Our work focuses on unsupervised and generative methods that address the following goals: (a) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (b) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (c) developing anomaly detection methods that leverage representations learned in (a).
Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
We develop a framework for novelty detection (ND) methods relying on deep embeddings, either discriminative or generative, and also propose a novel framework for assessing their performance.
Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation
In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV).
