Unsupervised learning with generative adversarial networks (GANs) has proven to be hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson χ 2 divergence. We also show that the derived objective function that yields minimizing the Pearson χ 2 divergence performs better than the classical one of using least squares for classification. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stably during the learning process. For evaluating the image quality, we conduct both qualitative and quantitative experiments, and the experimental results show that LSGANs can generate higher quality images than regular GANs. Furthermore, we evaluate the stability of LSGANs in two groups. One is to compare between LSGANs and regular GANs without gradient penalty. We conduct three experiments, including Gaussian mixture distribution, difficult architectures, and a newly proposed method -datasets with small variability, to illustrate the stability of LSGANs. The other one is to compare between LSGANs with gradient penalty (LSGANs-GP) and WGANs with gradient penalty (WGANs-GP). The experimental results show that LSGANs-GP succeed in training for all the difficult architectures used in WGANs-GP, including 101-layer ResNet.
Fiber microactuators are interesting in wide variety of emerging fields, including artificial muscles, biosensors, and wearable devices. In the present study, a robust, fast‐responsive, and humidity‐induced silk fiber microactuator is developed by integrating force‐reeling and yarn‐spinning techniques. The shape gradient, together with hierarchical rough surface, allows these silk fiber microactuators to respond rapidly to humidity. The silk fiber microactuator can reach maximum rotation speed of 6179.3° s−1 in 4.8 s. Such a response speed (1030 rotations per minute) is comparable with the most advanced microactuators. Moreover, this microactuator generates 2.1 W kg−1 of average actuation power, which is twice higher than fiber actuators constructed by cocoon silks. The actuating powers of silk fiber microactuators can be precisely programmed by controlling the number of fibers used. Lastly, theory predicts the observed performance merits of silk fiber microactuators toward inspiring the rational design of water‐induced microactuators.
Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.
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