Semi-supervised learning (SSL) effectively leverages unlabeled data and has been proven successful across various fields. Current safe SSL methods believe that unseen classes in unlabeled data harm the performance of SSL models. However, previous methods for assessing the impact of unseen classes on SSL model performance are flawed. They fix the size of the unlabeled dataset and adjust the proportion of unseen classes within the unlabeled data to assess the impact. This process contravenes the principle of controlling variables. Adjusting the proportion of unseen classes in unlabeled data alters the proportion of seen classes, meaning the decreased classification performance of seen classes may not be due to an increase in unseen class samples in the unlabeled data, but rather a decrease in seen class samples. Thus, the prior flawed assessment standard that "unseen classes in unlabeled data can damage SSL model performance" may not always hold true. This paper strictly adheres to the principle of controlling variables, maintaining the proportion of seen classes in unlabeled data while only changing the unseen classes across five critical dimensions, to investigate their impact on SSL models from global robustness and local robustness. Experiments demonstrate that unseen classes in unlabeled data do not necessarily impair the performance of SSL models; in fact, under certain conditions, unseen classes may even enhance them.

Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection, working to mitigate overconfident predictions of OOD data. These techniques rectifying anomalous activations, enhancing the distinguishability between in-distribution (ID) data and OOD data. However, they assume by default that every channel is necessary for OOD detection, and rectify anomalous activations in each channel. Empirical evidence has shown that there is a significant difference among various channels in OOD detection, and discarding some channels can greatly enhance the performance of OOD detection. Based on this insight, we propose Discriminability-Driven Channel Selection (DDCS), which leverages an adaptive channel selection by estimating the discriminative score of each channel to boost OOD detection. The discriminative score takes inter-class similarity and inter-class variance of training data into account. However, the estimation of discriminative score itself is susceptible to anomalous activations. To better estimate score, we pre-rectify anomalous activations for each channel mildly. The experimental results show that DDCS achieves state-of-the-art performance on CIFAR and ImageNet-1K benchmarks. Moreover, DDCS can generalize to different backbones and OOD scores.

There has been a boost in optoelectronic device technology that can leverage strengths of both optical and electronic worlds to support high-voltage and high-speed operation. It is critical to characterize the RF performance from the measured signals of these devices in order to evaluate their performance, optimize their designs and also aid in better understanding of the device physics. Conventional curve-fitting models either fail to fit measured signals with high accuracy or provide limited, if any, information about the device physics. Here, we propose a Prony-based curve-fitting method to characterize RF pulse measurements from such optoelectronic devices. The performance of the overall algorithm on measurement data shows high accuracy, with the capability to extract key pulse parameters such as full width at half maximum and rise time. Additionally, the capability of the method to extract time constants associated with semiconductor traps can help in better understanding of optoelectronic device physics.