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Found 9 papers, 3 papers with code
Improving Transferability of Adversarial Examples via Bayesian Attacks
Our ICLR work advocated for enhancing transferability in adversarial examples by incorporating a Bayesian formulation into model parameters, which effectively emulates the ensemble of infinitely many deep neural networks, while, in this paper, we introduce a novel extension by incorporating the Bayesian formulation into the model input as well, enabling the joint diversification of both the model input and model parameters.
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution.
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images.
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
Metric learning aims to learn a distance measure that can benefit distance-based methods such as the nearest neighbour (NN) classifier.
Information Theoretic Lower Bounds for Feed-Forward Fully-Connected Deep Networks
In this paper, we study the sample complexity lower bounds for the exact recovery of parameters and for a positive excess risk of a feed-forward, fully-connected neural network for binary classification, using information-theoretic tools.
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.
Towards Certified Robustness of Distance Metric Learning
Many existing methods consider maximizing or at least constraining a distance margin in the feature space that separates similar and dissimilar pairs of instances to guarantee their generalization ability.
Metric Learning via Maximizing the Lipschitz Margin Ratio
In this paper, we propose the Lipschitz margin ratio and a new metric learning framework for classification through maximizing the ratio.
Learning Local Metrics and Influential Regions for Classification
The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data.
