Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

In real-world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi-view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised multi-view representation learning model termed Collaborative Multi-View Information Bottleneck Networks (CMIB-Nets), which comprehensively explores the common latent structure and the view-specific intrinsic information, and discards the superfluous information in the data significantly improving the generalization capability of the model. Specifically, our proposed model relies on the information bottleneck principle to integrate the shared representation among different views and the view-specific representation of each view, prompting the multi-view complete representation and flexibly balancing the complementarity and consistency among multiple views. We conduct extensive experiments (including clustering analysis, robustness experiment, and ablation study) on real-world datasets, which empirically show promising generalization ability and robustness compared to state-of-the-arts.

Local discriminative representation is needed in many medical image analysis tasks such as identifying sub-types of lesion or segmenting detailed components of anatomical structures. However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks. In order to avoid the limitations of these two methods, we introduce local discrimination into unsupervised representation learning in this work. The model contains two branches: one is an embedding branch which learns an embedding function to disperse dissimilar pixels over a low-dimensional hypersphere; and the other is a clustering branch which learns a clustering function to classify similar pixels into the same cluster. These two branches are trained simultaneously in a mutually beneficial pattern, and the learnt local discriminative representations are able to well measure the similarity of local image regions. These representations can be transferred to enhance various downstream tasks. Meanwhile, they can also be applied to cluster anatomical structures from unlabeled medical images under the guidance of topological priors from simulation or other structures with similar topological characteristics. The effectiveness and usefulness of the proposed method are demonstrated by enhancing various downstream tasks and clustering anatomical structures in retinal images and chest X-ray images.KeywordsUnsupervised representation learningLocal discriminationTopological priors

Adversarial correlated autoencoder for unsupervised multi-view representation learning

Developing the utilized intelligent systems is increasingly important to learn effective text representations, especially extract the sentence features. Numerous previous studies have been concentrated on the task of sentence representation learning based on deep learning approaches. However, the present approaches are mostly proposed with the single task or replied on the labeled corpus when learning the embedding of the sentences. In this paper, we assess the factors in learning sentence representation and propose an efficient unsupervised learning framework with multi-task learning (USR-MTL), in which various text learning tasks are merged into the unitized framework. With the syntactic and semantic features of sentences, three different factors to some extent are reflected in the task of the sentence representation learning that is the wording, or the ordering of the neighbored sentences of a target sentence in other words. Hence, we integrate the word-order learning task, word prediction task, and the sentence-order learning task into the proposed framework to attain meaningful sentence embeddings. Here, the process of sentence embedding learning is reformulated as a multi-task learning framework of the sentence-level task and the two word-level tasks. Moreover, the proposed framework is motivated by an unsupervised learning algorithm utilizing the unlabeled corpus. Based on the experimental results, our approach achieves the state-of-the-art performances on the downstream natural language processing tasks compared to the popular unsupervised representation learning techniques. The experiments on representation visualization and task analysis demonstrate the effectiveness of the tasks in the proposed framework in creating reasonable sentence representations proving the capacity of the proposed unsupervised multi-task framework for the sentence representation learning.

Adversarial attacks and active defense on deep learning based identification of GaN power amplifiers under physical perturbation

We address adversarial attacks on the actuators at the joints of legged robots trained by deep reinforcement learning. The vulnerability to the joint attacks can significantly impact the safety and robustness of legged robots. In this study, we demonstrate that the adversarial perturbations to the torque control signals of the actuators can significantly reduce the rewards and cause walking instability in robots. To find the adversarial torque perturbations, we develop black-box adversarial attacks, where, the adversary cannot access the neural networks trained by deep reinforcement learning. The black box attack can be applied to legged robots regardless of the architecture and algorithms of deep reinforcement learning. We employ three search methods for the black-box adversarial attacks: random search, differential evolution, and numerical gradient descent methods. In experiments with the quadruped robot Ant-v2 and the bipedal robot Humanoid-v2, in OpenAI Gym environments, we find that differential evolution can efficiently find the strongest torque perturbations among the three methods. In addition, we realize that the quadruped robot Ant-v2 is vulnerable to the adversarial perturbations, whereas the bipedal robot Humanoid-v2 is robust to the perturbations. Consequently, the joint attacks can be used for proactive diagnosis of robot walking instability.

Artificial Intelligence (AI) has advanced in a rapid and exponential manner and AI is now revolutionizing the medical field, the development of self-driving cars, and the management of financial operations. A major reason for such achievement is the efficiency and performance of AI models, which can be drastically boosted through new learning methods. One of the most promising avenues is the unsupervised representation learning method that also offers the choice of leaping over the traditional supervised learning. Instead of supervised learning, where labeled data is the base upon which training of models takes place, unsupervised learning allows AI systems to get new insights out of raw, unlabeled data without any human intervention. This method teaches the AI system how to represent the data in an organized manner, thus enabling it to uncover hidden features and relations without anyone helping it. In effect, AI models, powered by unsupervised representation learning, can be quite successful in areas like clustering, anomaly detection, and feature extraction, frequently beating traditional methods in terms of their speed and accuracy. .The skill of finding deep structures in a dataset can unexpectedly influence many areas of the sciences, e.g., it can help us create a better diagnostic system in medicine or use it for decision-making when investing in the stock market

Recent major developments in semantic communication systems stem from integration of deep learning (DL) techniques. Following the discovery of capacity achieving codes, the primary motivation for adopting the semantic approach, which retrieves meaning without requiring an exact reconstruction, is its potential to further conserve resources such as bandwidth and power. In this paper, we propose a novel semantic communication framework for textual data over additive white Gaussian noise (AWGN) channels via DL. Our framework leverages the information bottleneck (IB) principle to balance minimizing bit transmission under wireless channel rate constraints with maximizing semantic information retention. Unlike previous works, we integrate the bilingual evaluation understudy (BLEU) sentence similarity score into the training objective to enhance model performance. In particular, inspired by knowledge distillation, we utilize large language models (LLMs) during training to transfer their knowledge of text semantics into our model. Using IB principle, we train a neural semantic encoder at the transmitter and a neural semantic decoder at the receiver that incorporates into its objective function the rate constraint together with the BLEU score and the knowledge encoded in the soft probabilities produced by the LLM. Through extensive experiments, our proposed framework demonstrates a notable improvement of up to 45% in text semantic similarity compared to state-of-the-art benchmarks operating at the same channel capacity, significantly outperforming traditional communication systems. Moreover, it exhibits robustness to variations in signal-to-noise ratio (SNR) and achieves significant gains across both low and medium SNR regimes.

With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited. And the interpretability of models is not strong enough. To tackle this issue, this paper proposes a Physics-bAsed unsupeRvised Contrastive rEpresentation Learning (PARCEL) method to speed up parallel MR imaging. Specifically, PARCEL has a parallel framework to contrastively learn two branches of model-based unrolling networks from augmented undersampled multi-coil k-space data. A sophisticated co-training loss with three essential components has been designed to guide the two networks in capturing the inherent features and representations for MR images. And the final MR image is reconstructed with the trained contrastive networks. PARCEL was evaluated on two vivo datasets and compared to five state-of-the-art methods. The results show that PARCEL is able to learn essential representations for accurate MR reconstruction without relying on fully sampled datasets. The code will be made available at https://github.com/ternencewu123/PARCEL.

Graph prototypical contrastive learning

Unsupervised multi-view graph representation learning with dual weight-net

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data. To improve the robustness against such distributional shifts, we propose a Model-Agnostic Recipe for Improving OOD generalizability of unsupervised graph contrastive learning methods, which we refer to as MARIO. MARIO introduces two principles aimed at developing distributional-shift-robust graph contrastive methods to overcome the limitations of existing frameworks: (i) Invariance principle that incorporates adversarial graph augmentation to obtain invariant representations and (ii) Information Bottleneck (IB) principle for achieving generalizable representations through refining representation contrasting. To the best of our knowledge, this is the first work that investigates the OOD generalization problem of graph contrastive learning, with a specific focus on node-level tasks. Through extensive experiments, we demonstrate that our method achieves state-of-the-art performance on the OOD test set, while maintaining comparable performance on the in-distribution test set when compared to existing approaches. Our codes are available at: https://github.com/ZhuYun97/MARIO.

CURL: Image Classification using co-training and Unsupervised Representation Learning

Adversarial attacks present a formidable challenge to the integrity of Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models, particularly in the domain of power quality disturbance (PQD) classification, necessitating the development of effective defense mechanisms. These attacks, characterized by their subtlety, can significantly degrade the performance of models critical for maintaining power system stability and efficiency. This study introduces the concept of adversarial attacks on CNN-LSTM models and emphasizes the critical need for robust defenses.We propose Input Adversarial Training (IAT) as a novel defense strategy aimed at enhancing the resilience of CNN-LSTM models. IAT involves training models on a blend of clean and adversarially perturbed inputs, intending to improve their robustness. The effectiveness of IAT is assessed through a series of comparisons with established defense mech-anisms, employing metrics such as accuracy, precision, recall, and F1-score on both unperturbed and adversarially modified datasets.The results are compelling: models defended with IAT exhibit remarkable improvements in robustness against adver-sarial attacks. Specifically, IAT-enhanced models demonstrated an increase in accuracy on adversarially perturbed data to 85%, a precision improvement to 86%, a recall rise to 85%, and an F1-score enhancement to 85.5%. These figures significantly surpass those achieved by models utilizing standard adversarial training (75% accuracy) and defensive distillation (70% accuracy), showcasing IAT’s superior capacity to maintain model accuracy under adversarial conditions.In conclusion, IAT stands out as an effective defense mechanism, significantly bolstering the resilience of CNN-LSTM models against adversarial perturbations. This research not only sheds light on the vulnerabilities of these models to adversarial attacks but also establishes IAT as a benchmark in defense strategy development, promising enhanced security and reliability for PQD classification and related applications.