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Found 5 papers, 1 papers with code
Difference-Seeking Generative Adversarial Network--Unseen Sample Generation
Unseen data, which are not samples from the distribution of training data and are difficult to collect, have exhibited importance in numerous applications, ({\em e. g.,} novelty detection, semi-supervised learning, and adversarial training).
Greedy Algorithms for Hybrid Compressed Sensing
Considering the fact that the one-bit CS is optimal for the direction estimation of signals under noise with a fixed bit budget and that the traditional CS is able to provide residue information and estimated signals, we focus on the design of greedy algorithms, which consist of the main steps of support detection and recovered signal update, for the hybrid CS in this paper.
Difference-Seeking Generative Adversarial Network
DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.
Fast Binary Embedding via Circulant Downsampled Matrix -- A Data-Independent Approach
Binary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power.
Fast Template Matching by Subsampled Circulant Matrix
Template matching is widely used for many applications in image and signal processing and usually is time-critical.
