Node-level out-of-distribution (OOD) detection on graphs has received significant attention from the machine learning community. However, previous approaches are evaluated using unrealistic benchmarks that consider only randomly selected OOD nodes, failing to reflect the interactions among nodes. In this paper, we introduce a new challenging task to model the interactions of OOD nodes in a graph, termed spreading OOD detection, where a newly emerged OOD node spreads its property to neighboring nodes. We curate realistic benchmarks by employing the epidemic spreading models that simulate the spreading of OOD nodes on the graph. We also showcase a ``Spreading COVID-19" dataset to demonstrate the applicability of spreading OOD detection in real-world scenarios. Furthermore, to effectively detect spreading OOD samples under the proposed benchmark setup, we present a new approach called energy distribution-based detector (EDBD), which includes a novel energy-aggregation scheme. EDBD is designed to mitigate undesired mixing of OOD scores between in-distribution (ID) and OOD nodes. Our extensive experimental results demonstrate the superiority of our approach over state-of-the-art methods in both spreading OOD detection and conventional node-level OOD detection tasks across seven benchmark datasets. The source code is available at https://github.com/daehoum1/edbd.

Data imbalance remains a fundamental challenge in real-world machine learning. However, most existing work has focused on classification, leaving imbalanced regression underexplored despite its importance in many applications. To address this gap, we propose PRIME, a framework that leverages learnable proxies to construct a balanced and well-ordered feature space for imbalanced regression. At its core, PRIME arranges proxies to be uniformly distributed in the feature space while preserving the ordinal structure of regression targets, and then aligns each sample feature to its corresponding proxy. By using proxies as reference points, PRIME induces the desired structure of learned representations, promoting better generalization, especially in underrepresented target regions. Moreover, since proxy-based alignment resembles classification, PRIME enables the seamless application of class imbalance techniques to regression, facilitating more balanced feature learning. Extensive experiments demonstrate the effectiveness and broad applicability of PRIME, achieving state-of-the-art performance on four real-world regression benchmark datasets across diverse target domains.

In this paper, we address learning tasks on graphs with missing features, enhancing the applicability of graph neural networks to real-world graph-structured data. We identify a critical limitation of existing imputation methods based on feature propagation: they produce channels with nearly identical values within each channel, and these low-variance channels contribute very little to performance in graph learning tasks. To overcome this issue, we introduce synthetic features that target the root cause of low-variance channel production, thereby increasing variance in these channels. By preventing propagation-based imputation methods from generating meaningless feature values shared across all nodes, our synthetic feature propagation scheme mitigates significant performance degradation, even under extreme missing rates. Extensive experiments demonstrate the effectiveness of our approach across various graph learning tasks with missing features, ranging from low to extremely high missing rates. Additionally, we provide both empirical evidence and theoretical proof to validate the low-variance problem. The source code is available at https://github.com/daehoum1/fisf.

Neural networks are often prone to bias toward spurious correlations inherent in a dataset, thus failing to generalize unbiased test criteria. A key challenge to resolving the issue is the significant lack of bias-conflicting training data (i. e., samples without spurious correlations). In this paper, we propose a novel data augmentation approach termed Bias-Adversarial augmentation (BiasAdv) that supplements bias-conflicting samples with adversarial images. Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate syn-thetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model. Specifically, we formulate an optimization problem for gen-erating adversarial images that attack the predictions of an auxiliary biased model without ruining the predictions of the desired debiased model. Despite its simplicity, we find that BiasAdv can generate surprisingly useful synthetic bias-conflicting samples, allowing the debiased model to learn generalizable representations. Furthermore, BiasAdv does not require any bias annotations or prior knowledge of the bias type, which enables its broad applicability to existing debiasing methods to improve their performances. Our extensive experimental results demonstrate the superiority of BiasAdv, achieving state-of-the-art performance on four popular benchmark datasets across various bias domains.

Deep learning algorithms require large amounts of labeled data for effective performance, but the presence of noisy labels often significantly degrade their performance. Although recent studies on designing a robust objective function to label noise, known as the robust loss method, have shown promising results for learning with noisy labels, they suffer from the issue of underfitting not only noisy samples but also clean ones, leading to suboptimal model performance. To address this issue, we propose a novel learning framework that selectively suppresses noisy samples while avoiding underfitting clean data. Our framework incorporates label confidence as a measure of label noise, enabling the network model to prioritize the training of samples deemed to be noise-free. The label confidence is based on the robust loss methods, and we provide theoretical evidence that our method can reach the optimal point of the robust loss, subject to certain conditions. Furthermore, the proposed method is generalizable and can be combined with existing robust loss methods, making it suitable for a wide range of applications of learning with noisy labels. We evaluate our approach on both synthetic and real-world datasets, and the experimental results demonstrate its effectiveness in achieving outstanding classification performance compared to state-of-the-art methods.

In this paper, we propose Hypergraph-Induced Semantic Tuplet (HIST) loss for deep metric learning that leverages the multilateral semantic relations of multiple samples to multiple classes via hypergraph modeling. We formulate deep metric learning as a hypergraph node classification problem in which each sample in a mini-batch is regarded as a node and each hyperedge models class-specific semantic relations represented by a semantic tuplet. Unlike previous graph-based losses that only use a bundle of pairwise relations, our HIST loss takes advantage of the multilateral semantic relations provided by the semantic tuplets through hypergraph modeling. Notably, by leveraging the rich multilateral semantic relations, HIST loss guides the embedding model to learn class-discriminative visual semantics, contributing to better generalization performance and model robustness against input corruptions. Extensive experiments and ablations provide a strong motivation for the proposed method and show that our HIST loss leads to improved feature learning, achieving state-of-the-art results on three widely used benchmarks. Code is available at https://github.com/ljin0429/HIST.

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.

In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that alleviates the influence of samples that cause an overfitted decision boundary. The proposed loss efficiently improves the performance of any type of imbalance learning methods. In experiments on multiple benchmark data sets, we demonstrate the validity of our method and reveal that the proposed loss outperforms the state-of-the-art cost-sensitive loss methods. Furthermore, since our loss is not restricted to a specific task, model, or training method, it can be easily used in combination with other recent resampling, meta-learning, and cost-sensitive learning methods for class-imbalance problems. Our code is made available at https://github.com/pseulki/IB-Loss.

In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a large dataset. However, it is relatively difficult to sufficiently finetune the low-level layers of the network due to the gradient vanishing problem. In this work, we propose a novel fine-tuning strategy that allows low-level layers to be sufficiently trained by rolling back the weights of high-level layers to their initial pre-trained weights. Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks. The improved performance of the proposed strategy is validated via several experiments. Furthermore, without any addons such as pose estimation or segmentation, our strategy exhibits state-of-the-art performance using only vanilla deep convolutional neural network architecture.

This paper proposes a selective ensemble deep network architecture for crowd density estimation and people counting. In contrast to existing deep network-based methods, the proposed method incorporates two sub-networks for local density estimation: one to learn sparse density regions and one to learn dense density regions. Locally estimated density maps from the two sub-networks are selectively combined in ensemble fashion using a gating network to estimate an initial crowd density map. The initial density map is refined as a high resolution map, using another sub-network that draws on contextual information in the image. In training, a novel adaptive loss scheme is applied to resolve an ambiguity in the crowded region. the proposed scheme improves both density map accuracy and counting accuracy by adjusting the weighting value between density loss and counting loss according to the degree of crowdness and training epochs. Experiments using public datasets confirm that the proposed method outperforms state-of-the-art methods. Through self-evaluation, the effectiveness of each part in the network is also verified.

This paper proposes a moving object detection algorithm for a monocular dashcam mounted on a vehicle. To deal with dynamic changes of the scene from the dashcam, we propose a new scheme inspired by human-attention inclination for change detection. Humans do not build a detailed visual representation and perceive a change of the scene based on the structure of an interesting region. In this perspective, our method focuses on a sky and road region of the scene and builds an abstracted background model, which is updated with a spatially adaptive learning rate according to the center-focused tendency of the human gaze. To improve the robustness of detection, the final detection map is refined by combining the results from twin processes applied to the original image and the median-filtered image, respectively. In experiments, we have found that our method outperforms state-of-the-art methods qualitatively and quantitatively on a realistic dashcam video.