Graph Contrastive Learning (GCL) emerges as a powerful approach in recommendation systems, leveraging graph structures to learn effective representations. However, existing contrastive sampling strategies often introduce unintended biases, most notably, the misclassification of genuine positive samples as negatives, which undermines representation quality and overall recommendation performance. Accordingly, this paper revisits the conventional contrastive sampling and introduces Negative-Free Sampling for Graph Contrastive Learning (NFS). NFS adopts a two-stage sampling strategy that selectively identifies and utilizes only positive instances during training. By removing reliance on negative samples, it effectively mitigates misclassification bias and improves the semantic alignment between related representations. In addition, a comprehensive theoretical analysis is also provided to establish the robustness of NFS against representation collapse. Experimental results on three benchmarks demonstrate that NFS consistently outperforms or performs state-of-the-art methods, achieving up to a 14.2% relative improvement across evaluated datasets. In addition, a detailed ablation study is also provided to examine how exclusively leveraging positive samples contributes to the efficiency of GCL. The results further demonstrate the plug-and-play nature of the proposed method and its resilience to noisy data.

Adversarial examples pose significant challenges for Natural Language Processing (NLP) model robustness, often causing notable performance degradation. While various detection methods have been proposed with the aim of differentiating clean and adversarial inputs, they often require fine-tuning with ample data, which is problematic for low-resource scenarios. To alleviate this issue, a Subspace Clustering based Adversarial Detector (termed SCAD) is proposed in this paper, leveraging a union of subspaces to model the clean data distribution. Specifically, SCAD estimates feature distribution across semantic subspaces, assigning unseen examples to the nearest one for effective discrimination. The construction of semantic subspaces does not require many observations and hence ideal for the low-resource setting.

Image-based virtual try-on aims to transfer a clothes onto a person while preserving both person's and cloth's attributes. However, the existing methods to realize this task require a target clothes, which cannot be obtained in most cases. To address this issue, we propose a novel user-friendly virtual try-on network (UF-VTON), which only requires a person image and an image of another person wearing a target clothes to generate a result of the person wearing the target clothes. Specifically, we adopt a knowledge distillation scheme to construct a new triple dataset for supervised learning, propose a new three-step pipeline (coarse synthesis, clothing alignment, and refinement synthesis) for try-on task, and utilize an end-to-end training strategy to further refine the results. In particular, we design a new synthesis network that includes both CNN blocks and swin-transformer blocks to capture global and local information and generate highly-realistic try-on images. Qualitative and quantitative experiments show that our method achieves the state-of-the-art virtual try-on performance.