深度学习方法通常需要大量的培训数据和缺乏可解释性。在本文中，我们提出了一种用于医学图像分类的新颖知识蒸馏和模型解释框架，共同解决了上述两个问题。具体而言，为了解决数据饥饿的问题，通过从繁琐的预训练教师模型中蒸馏知识来学习一个小学生模型。为了解释教师模型并协助学生的学习，引入了解释器模块，以突出显示对教师模型的预测很重要的输入。此外，联合框架通过来自信息理论的角度来源的原理方式训练。与眼底数据集上的最先进方法相比，我们的框架优于知识蒸馏和模型解释任务。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.

In this work, we propose a new approach that combines data from multiple sensors for reliable obstacle avoidance. The sensors include two depth cameras and a LiDAR arranged so that they can capture the whole 3D area in front of the robot and a 2D slide around it. To fuse the data from these sensors, we first use an external camera as a reference to combine data from two depth cameras. A projection technique is then introduced to convert the 3D point cloud data of the cameras to its 2D correspondence. An obstacle avoidance algorithm is then developed based on the dynamic window approach. A number of experiments have been conducted to evaluate our proposed approach. The results show that the robot can effectively avoid static and dynamic obstacles of different shapes and sizes in different environments.

Out-of-distribution (OOD) generalisation aims to build a model that can well generalise its learnt knowledge from source domains to an unseen target domain. However, current image classification models often perform poorly in the OOD setting due to statistically spurious correlations learning from model training. From causality-based perspective, we formulate the data generation process in OOD image classification using a causal graph. On this graph, we show that prediction P(Y|X) of a label Y given an image X in statistical learning is formed by both causal effect P(Y|do(X)) and spurious effects caused by confounding features (e.g., background). Since the spurious features are domain-variant, the prediction P(Y|X) becomes unstable on unseen domains. In this paper, we propose to mitigate the spurious effect of confounders using front-door adjustment. In our method, the mediator variable is hypothesized as semantic features that are essential to determine a label for an image. Inspired by capability of style transfer in image generation, we interpret the combination of the mediator variable with different generated images in the front-door formula and propose novel algorithms to estimate it. Extensive experimental results on widely used benchmark datasets verify the effectiveness of our method.

The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models are built upon high-quality (HQ) data as desired outputs, making them unfit for in-the-wild low-quality (LQ) images, which are common inputs for manipulation. In this work, we bridge this gap by proposing a novel GAN structure that allows for generating images with controllable quality. The network can synthesize various image degradation and restore the sharp image via a quality control code. Our proposed QC-StyleGAN can directly edit LQ images without altering their quality by applying GAN inversion and manipulation techniques. It also provides for free an image restoration solution that can handle various degradations, including noise, blur, compression artifacts, and their mixtures. Finally, we demonstrate numerous other applications such as image degradation synthesis, transfer, and interpolation.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

语义分割是开发医学图像诊断系统的重要任务。但是，构建注释的医疗数据集很昂贵。因此，在这种情况下，半监督方法很重要。在半监督学习中，标签的质量在模型性能中起着至关重要的作用。在这项工作中，我们提出了一种新的伪标签策略，可提高用于培训学生网络的伪标签的质量。我们遵循多阶段的半监督训练方法，该方法在标记的数据集上训练教师模型，然后使用训练有素的老师将伪标签渲染用于学生培训。通过这样做，伪标签将被更新，并且随着培训的进度更加精确。上一个和我们的方法之间的关键区别在于，我们在学生培训过程中更新教师模型。因此，在学生培训过程中，提高了伪标签的质量。我们还提出了一种简单但有效的策略，以使用动量模型来提高伪标签的质量 - 训练过程中原始模型的慢复制版本。通过应用动量模型与学生培训期间的重新渲染伪标签相结合，我们在五个数据集中平均达到了84.1％的骰子分数（即Kvarsir，CVC-ClinicdB，Etis-laribpolypdb，cvc-colondb，cvc-colondb，cvc-colondb和cvc-300）和CVC-300）只有20％的数据集用作标记数据。我们的结果超过了3％的共同实践，甚至在某些数据集中取得了完全监督的结果。我们的源代码和预培训模型可在https://github.com/sun-asterisk-research/online学习SSL上找到

在本文中，我们提出了一个新的低级矩阵分解模型，称为有界的单纯形成矩阵分解（BSSMF）。给定输入矩阵$ x $和一个分解等级$ r $，BSSMF寻找带有$ r $ lum $ $ columns的矩阵$ w $和a矩阵$ h $，带有$ r $行，以便$ x \ lot在$ w $的每一列中，都有边界，也就是说，它们属于给定的间隔，$ h $的列属于概率单纯词，即，$ h $是列随机。 BSSMF概括了非负矩阵分解（NMF）和单纯结构的矩阵分解（SSMF）。当输入矩阵$ x $的条目属于给定间隔时，BSSMF特别适合。例如，当$ x $的行代表图像时，或$ x $是一个额定矩阵，例如在Netflix和Movielens数据集中，其中$ x $的条目属于Interval $ [1,5] $。单纯结构的矩阵$ h $不仅导致易于理解的分解，从而提供了$ x $的列的软聚类，而且暗示着$ wh $的每个列的条目属于与$的列的相同间隔W $。在本文中，我们首先提出了BSSMF的快速算法，即使在$ x $中缺少数据的情况下。然后，我们为BSSMF提供可识别性条件，也就是说，我们提供了BSSMF承认独特分解的条件，直到微不足道的歧义。最后，我们说明了BSSMF对两个应用程序的有效性：在一组图像中提取特征，以及推荐系统的矩阵完成问题。

为了提倡研究基于深度学习的机器故障检测系统的研究，我们根据微小的声音数据集对拟议系统进行了案例研究。我们的案例研究调查了一个变异自动编码器（VAE），用于增强Valmet AB的小型钻头数据集。一个气门数据集包含134种声音，分为两类：从Valmet AB的一台钻机中记录的“异常”和“正常”，这是瑞典Sundsvall的一家公司，该公司为生物燃料的生产提供设备和流程。使用深度学习模型来检测如此小的声音数据集上的故障钻头通常没有成功。我们采用了VAE来通过合成原始声音的新声音来增加微小数据集中的声音数量。增强数据集是通过将这些合成的声音与原始声音相结合来创建的。我们使用了一个高通滤波器，其通带频率为1000 Hz和一个具有22 \ kern的Passband频率的低通滤波器0.16667EM000 Hz，以在增强数据集中的预处理声音中，然后将其转换为MEL频谱图。然后使用这些MEL频谱图对预训练的2D-CNN ALEXNET进行训练。与使用原始的小声音数据集进行训练预先训练的Alexnet时，使用增强声音数据集将CNN模型的分类结果提高了6.62 \％（94.12 \％（在增强数据集对87.5 \％训练的原始训练时，接受了87.5 \％）数据集）。