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Found 21 papers, 8 papers with code
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
To enable risk-averse predictions from a DG classifier, we propose a novel inference procedure, Test-Time Neural Style Smoothing (TT-NSS), that uses a "style-smoothed" version of the DG classifier for prediction at test time.
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation
The adoption of UDA with multiple sources can simultaneously enrich the training set and bridge the domain gap between different skin lesion datasets, which vary due to distinct acquisition protocols.
Analysis of Task Transferability in Large Pre-trained Classifiers
We propose a novel Task Transfer Analysis approach that transforms the source distribution (and classifier) by changing the class prior distribution, label, and feature spaces to produce a new source distribution (and classifier) and allows us to relate the loss of the downstream task (i. e., transferability) to that of the source task.
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation
Specifically, we leverage the contrastive loss to learn representations of both the foreground and background regions in the images.
Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future
Recurrent models are frequently being used in online tasks such as autonomous driving, and a comprehensive study of their vulnerability is called for.
On Certifying and Improving Generalization to Unseen Domains
This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild.
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines
To alleviate this issue, we propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data.
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target.
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation
However, the limited effect of poisoning is restricted to the setting where training and test data are from the same distribution.
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks
Electronic Health Records (EHRs) provide a wealth of information for machine learning algorithms to predict the patient outcome from the data including diagnostic information, vital signals, lab tests, drug administration, and demographic information.
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inputs.
How Robust are Randomized Smoothing based Defenses to Data Poisoning?
Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation\cite{cohen2019certified}, MACER\cite{zhai2020macer}, and SmoothAdv\cite{salman2019provably} that achieve high certified adversarial robustness.
Penalty Method for Inversion-Free Deep Bilevel Optimization
We present results on data denoising, few-shot learning, and training-data poisoning problems in a large-scale setting.
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robust learning.
Fast Interactive Image Retrieval using large-scale unlabeled data
Active learning reduces the number of user interactions by querying the labels of the most informative points and GSSL allows to use abundant unlabeled data along with the limited labeled data provided by the user.
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
We demonstrate the minimax defense with two types of attack classes -- gradient-based and neural network-based attacks.
Minimax Filter: Learning to Preserve Privacy from Inference Attacks
The paper proposes a novel filter-based mechanism which preserves privacy of continuous and high-dimensional attributes against inference attacks.
Learning Privately from Multiparty Data
How can we build an accurate and differentially private global classifier by combining locally-trained classifiers from different parties, without access to any party's private data?
Probabilistic Zero-shot Classification with Semantic Rankings
In this paper we propose a non-metric ranking-based representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources.
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive.
Extended Grassmann Kernels for Subspace-Based Learning
Subspace-based learning problems involve data whose elements are linear subspaces of a vector space.
