Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification

Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem. However, most of these deep learning approaches do not take full consideration of either the hyperedge information or the original relationships among nodes and hyperedges. In this article, we present a simple yet effective semi-supervised node classification method named Hypergraph Convolution on Nodes-Hyperedges network, which performs filtering on both nodes and hyperedges as well as recovers the original hypergraph with the least information loss. Instead of only reducing the cross-entropy loss over the labeled samples as most previous approaches do, we additionally consider the hypergraph reconstruction loss as prior information to improve prediction accuracy. As a result, by taking both the cross-entropy loss on the labeled samples and the hypergraph reconstruction loss into consideration, we are able to achieve discriminative latent data representations for training a classifier. We perform extensive experiments on the semi-supervised node classification problem and compare the proposed method with state-of-the-art algorithms. The promising results demonstrate the effectiveness of the proposed method.

Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment

<p>Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper therefore proposes the Gaussian clouded logit adjustment by Gaussian perturbing different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in the classifier, we accordingly propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method.</p><br>

Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment

<p>Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper therefore proposes the Gaussian clouded logit adjustment by Gaussian perturbing different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in the classifier, we accordingly propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method.</p><br>

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.

SSDAN: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification

We present a new method for multi-source semi-supervised domain adaptation in remote sensing scene classification. The method consists of a pre-trained convolutional neural network (CNN) model, namely EfficientNet-B3, for the extraction of highly discriminative features, followed by a classification module that learns feature prototypes for each class. Then, the classification module computes a cosine distance between feature vectors of target data samples and the feature prototypes. Finally, the proposed method ends with a Softmax activation function that converts the distances into class probabilities. The feature prototypes are also divided by a temperature parameter to normalize and control the classification module. The whole model is trained on both the unlabeled and labeled target samples. It is trained to predict the correct classes utilizing the standard cross-entropy loss computed over the labeled source and target samples. At the same time, the model is trained to learn domain invariant features using another loss function based on entropy computed over the unlabeled target samples. Unlike the standard cross-entropy loss, the new entropy loss function is computed on the model’s predicted probabilities and does not need the true labels. This entropy loss, called minimax loss, needs to be maximized with respect to the classification module to learn features that are domain-invariant (hence removing the data shift), and at the same time, it should be minimized with respect to the CNN feature extractor to learn discriminative features that are clustered around the class prototypes (in other words reducing intra-class variance). To accomplish these maximization and minimization processes at the same time, we use an adversarial training approach, where we alternate between the two processes. The model combines the standard cross-entropy loss and the new minimax entropy loss and optimizes them jointly. The proposed method is tested on four RS scene datasets, namely UC Merced, AID, RESISC45, and PatternNet, using two-source and three-source domain adaptation scenarios. The experimental results demonstrate the strong capability of the proposed method to achieve impressive performance despite using only a few (six in our case) labeled target samples per class. Its performance is already better than several state-of-the-art methods, including RevGrad, ADDA, Siamese-GAN, and MSCN.

Building Outline Extraction Directly Using the U2-Net Semantic Segmentation Model from High-Resolution Aerial Images and a Comparison Study

Deep learning techniques have greatly improved the efficiency and accuracy of building extraction using remote sensing images. However, high-quality building outline extraction results that can be applied to the field of surveying and mapping remain a significant challenge. In practice, most building extraction tasks are manually executed. Therefore, an automated procedure of a building outline with a precise position is required. In this study, we directly used the U2-net semantic segmentation model to extract the building outline. The extraction results showed that the U2-net model can provide the building outline with better accuracy and a more precise position than other models based on comparisons with semantic segmentation models (Segnet, U-Net, and FCN) and edge detection models (RCF, HED, and DexiNed) applied for two datasets (Nanjing and Wuhan University (WHU)). We also modified the binary cross-entropy loss function in the U2-net model into a multiclass cross-entropy loss function to directly generate the binary map with the building outline and background. We achieved a further refined outline of the building, thus showing that with the modified U2-net model, it is not necessary to use non-maximum suppression as a post-processing step, as in the other edge detection models, to refine the edge map. Moreover, the modified model is less affected by the sample imbalance problem. Finally, we created an image-to-image program to further validate the modified U2-net semantic segmentation model for building outline extraction.

Shannon Entropy Loss in Mixed-Radix Conversions

This paper models a translation for base-2 pseudorandom number generators (PRNGs) to mixed-radix uses such as card shuffling. In particular, we explore a shuffler algorithm that relies on a sequence of uniformly distributed random inputs from a mixed-radix domain to implement a Fisher–Yates shuffle that calls for inputs from a base-2 PRNG. Entropy is lost through this mixed-radix conversion, which is assumed to be surjective mapping from a relatively large domain of size 2J to a set of arbitrary size n. Previous research evaluated the Shannon entropy loss of a similar mapping process, but this previous bound ignored the mixed-radix component of the original formulation, focusing only on a fixed n value. In this paper, we calculate a more precise formula that takes into account a variable target domain radix, n, and further derives a tighter bound on the Shannon entropy loss of the surjective map, while demonstrating monotonicity in a decrease in entropy loss based on increased size J of the source domain 2J. Lastly, this formulation is used to specify the optimal parameters to simulate a card-shuffling algorithm with different test PRNGs, validating a concrete use case with quantifiable deviations from maximal entropy, making it suitable to low-power implementation in a casino.