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Found 7 papers, 5 papers with code
Stratified Adversarial Robustness with Rejection
We theoretically analyze the stratified rejection setting and propose a novel defense method -- Adversarial Training with Consistent Prediction-based Rejection (CPR) -- for building a robust selective classifier.
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled data, and then learns simple predictors on top of the representation using small labeled data from the downstream tasks.
Concept-based Explanations for Out-Of-Distribution Detectors
Based on these metrics, we propose an unsupervised framework for learning a set of concepts that satisfy the desired properties of high detection completeness and concept separability, and demonstrate its effectiveness in providing concept-based explanations for diverse off-the-shelf OOD detectors.
Revisiting Adversarial Robustness of Classifiers With a Reject Option
Motivated by this metric, we propose novel loss functions and a robust training method -- \textit{stratified adversarial training with rejection} (SATR) -- for a classifier with reject option, where the goal is to accept and correctly-classify small input perturbations, while allowing the rejection of larger input perturbations that cannot be correctly classified.
Fast and Sample-Efficient Domain Adaptation for Autoencoder-Based End-to-End Communication
In this paper, we address the setting where the target domain has only limited labeled data from a distribution that is expected to change frequently.
Few-Shot Domain Adaptation For End-to-End Communication
The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be an effective approach.
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
We propose an unsupervised anomaly detection framework based on the internal DNN layer representations in the form of a meta-algorithm with configurable components.
