The concepts of conditional mutual information (CMI) and normalized CMI (NCMI) are introduced to measure the concentration and separation performance of a classification deep neural network (DNN) in the output probability distribution space of the DNN, where CMI and the ratio between CMI and NCMI represent the intraclass concentration and interclass separation of the DNN, respectively. By using NCMI to evaluate popular DNNs pretrained over CIFAR-100 and ImageNet in the literature, it is shown that their validation accuracies are more or less inversely proportional to their NCMI values. Based on this observation, the standard deep learning (DL) framework is further modified to minimize the standard cross entropy (CE) function subject to an NCMI constraint, yielding CMI constrained DL (CMIC-DL). A novel alternating learning algorithm is proposed to solve such a constrained optimization problem. Extensive experimental results show that DNNs trained within CMIC-DL outperform the state-of-the-art models trained within the standard DL and other loss functions in the literature in terms of both accuracy and robustness against adversarial attacks. In addition, visualizing the evolution of the learning process through the lens of CMI and NCMI is also advocated.

It is believed that in knowledge distillation (KD), the role of the teacher is to provide an estimate for the unknown Bayes conditional probability distribution (BCPD) to be used in the student training process. Conventionally, this estimate is obtained by training the teacher using maximum log-likelihood (MLL) method. To improve this estimate for KD, in this paper we introduce the concept of conditional mutual information (CMI) into the estimation of BCPD and propose a novel estimator called the maximum CMI (MCMI) method. Specifically, in MCMI estimation, both the log-likelihood and CMI of the teacher are simultaneously maximized when the teacher is trained. In fact, maximizing the teacher's CMI value ensures that the teacher can effectively capture the contextual information within the images, and for visualizing this information, we deploy Eigen-CAM. Via conducting a thorough set of experiments, we show that by employing a teacher trained via MCMI estimation rather than one trained via MLL estimation in various state-of-the-art KD frameworks, the student's classification accuracy consistently increases, with the gain of up to 3.32\%. This suggests that the teacher's BCPD estimate provided by MCMI method is more accurate than that provided by MLL method. In addition, we show that such improvements in the student's accuracy are more drastic in zero-shot and few-shot settings. Notably, the student's accuracy increases with the gain of up to 5.72\% when 5\% of the training samples are available to student (few-shot), and increases from 0\% to as high as 84\% for an omitted class (zero-shot).

Federated learning (FL) is a promising technology wherein edge devices/clients collaboratively train a machine learning model under the orchestration of a central server. However, due to the inherent data heterogeneity among clients, local datasets on individual clients often exhibit class imbalance, i.e., samples from majority classes vastly outnumber those from minority classes. This imbalance significantly diminishes the performance of the trained model. To understand why, we first closely examine the output probability distribution clusters of the local deep neural networks (DNNs) in the probability space over the label set, and observe that for class imbalanced datasets, FL has two interesting phenomena: (1) dispersion problem-clusters corresponding to minority classes tend to disperse; and (2) gravity problem-clusters corresponding to minority classes are drawn toward those of majority classes. To overcome these two problems, we then introduce information quantities into FL, propose a new information theoretic loss function for FL, and develop a new FL framework called Fed-IT. It is shown that Fed-IT significantly outperforms previous counterparts, while maintaining client privacy.

In this paper, we introduce the notions of conditional mutual information (CMI) and normalized conditional mutual information (NCMI) for classification deep neural networks (DNNs). In particular, CMI and the ratio between CMI and NCMI quantify the intra-class concentration and inter-class separation of a DNN in its output probability distribution space, respectively. Utilizing NCMI to assess widely recognized DNNs pre-trained on ImageNet reveals a notable inverse relationship between their validation accuracies and NCMI values on the ImageNet validation dataset. Building upon this insight, the conventional deep learning (DL) framework is modified by minimizing the standard cross-entropy function while imposing an NCMI constraint. This refinement results in a novel approach known as CMI-constrained deep learning (CMIC-DL). Comprehensive experimental findings demonstrate that DNNs trained using CMIC-DL outperform state-of-the-art models trained within standard DL and other loss functions in the literature. In addition, some semantic meaning of CMI is also discovered.

CTR prediction, which aims to estimate the probability that a user will click an item, plays a crucial role in online advertising and recommender system. Feature interaction modeling based and user interest mining based methods are the two kinds of most popular techniques that have been extensively explored for many years and have made great progress for CTR prediction. However, (1) feature interaction based methods which rely heavily on the co-occurrence of different features, may suffer from the feature sparsity problem (i.e., many features appear few times); (2) user interest mining based methods which need rich user behaviors to obtain user's diverse interests, are easy to encounter the behavior sparsity problem (i.e., many users have very short behavior sequences). To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems. We further propose a Dual Graph enhanced Embedding Neural Network(DG-ENN) for CTR prediction. Dual Graph enhanced Embedding exploits the strengths of graph representation with two carefully designed learning strategies (divide-and-conquer, curriculum-learning-inspired organized learning) to refine the embedding. We conduct comprehensive experiments on three real-world industrial datasets. The experimental results show that our proposed DG-ENN significantly outperforms state-of-the-art CTR prediction models. Moreover, when applying to state-of-the-art CTR prediction models, Dual graph enhanced embedding always obtains better performance. Further case studies prove that our proposed dual graph enhanced embedding could alleviate the feature sparsity and behavior sparsity problems. Our framework will be open-source based on MindSpore in the near future.