Deep neural networks are prone to memorizing incorrect labels during training, which degrades their generalizability. Although recent methods have combined sample selection with semi-supervised learning (SSL) to exploit the memorization effect — where networks learn from clean data before noisy data — they cannot correct selection errors once a sample is misclassified. We propose asymmetric co-teaching with different architectures and machine unlearning (ACD-U), a robust learning framework that integrates different model architectures with machine unlearning. ACD-U addresses the aforementioned limitation through two core mechanisms. First, it pairs a Contrastive Language-Image Pretraining (CLIP)-pretrained vision Transformer (ViT) with a convolutional neural network (CNN), leveraging their complementary learning behaviors: the ViT provides stable predictions, whereas the CNN remains adaptive throughout training. This asymmetry, with the ViT trained only on clean samples and the CNN trained through SSL, effectively mitigates confirmation bias. Second, selective unlearning enables post hoc error correction by identifying incorrectly memorized samples through loss trajectory analysis and CLIP consistency checks, and removing their influence via Kullback–Leibler divergence-based forgetting. This approach shifts the learning paradigm from passive error avoidance to active error correction. ACD-U achieves a 35% relative improvement over DivideMix on CIFAR-100 under 90% symmetric noise, the best results across all noise rates on Red Mini-ImageNet, and top accuracy on both Clothing1M and WebVision. Ablation analyses confirm that the pretrained ViT reduces critical sample misclassifications to approximately one-sixth of those of DivideMix, and that the unlearning and asymmetric co-teaching contribute complementarily across different noise regimes. These results suggest that ACD-U can improve the reliability of deep learning models trained on large-scale web-collected datasets, where label noise is unavoidable. The code is publicly available at https://github.com/meruemon/ACD-U .

The widespread dissemination of fake news on social media has substantial economic and social implications. Although traditional propagation-based methods employing graph neural networks show promise for fake news detection, they disregard the influence of confirmation bias in the spread of fake news between users with similar viewpoints. This paper presents a detection approach that accounts for opinion similarity between users by scrutinizing their stances toward news articles and users’ post interactions. Using a graph transformer network, our method simultaneously extracts global structural information and interactions of similar stances. Furthermore, it addresses the challenges of stance analysis targeting microblogs while minimizing the effect of poorly represented stance features. We evaluated our approach using custom-crawled Twitter data and the benchmark FibVID dataset. It demonstrated a marked improvement in detection performance compared with conventional methods, including state-of-the-art methods.

This paper proposes a graph-based Web video search reranking method through consistency analysis using spectral clustering. Graph-based reranking is effective for refining text-based video search results. Generally, this approach constructs a graph where the vertices are videos and the edges reflect their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes the smoothness of pairwise ranking scores between adjacent nodes. However, since the overall consistency is measured by aggregating the individual consistency over each pair, errors in score estimation increase when noisy samples are included within their neighbors. To deal with the noisy samples, different from the conventional methods, the proposed method models the global consistency of the graph structure. Specifically, in order to detect this consistency, the propose method introduces a spectral clustering algorithm which can detect video groups, whose videos have strong semantic correlation, on the graph. Furthermore, a new regularization term, which smooths ranking scores within the same group, is introduced to the reranking framework. Since score regularization is performed by both local and global aspect simultaneously, the accurate score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.

Graph-based reranking is effective for refining text-based video search results by making use of the social network structure. Unlike previous works which only focus on an individual video graph, the proposed method leverages the mutual reinforcement of heterogeneous graphs, such as videos and their associated tags obtained by social influence mining. Specifically, propagation of information relevancy across different modalities is performed by exchanging information of inter- and intra-relations among heterogeneous graphs. The proposed method then formulates the video search reranking as an optimization problem from the perspective of Bayesian framework. Furthermore, in order to model the consistency over the modified video graph topology, a local learning regularization with a social community detection scheme is introduced to the framework. Since videos within the same social community have strong semantic correlation, the consistency score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.