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Found 6 papers, 4 papers with code
Catastrophic Overfitting: A Potential Blessing in Disguise
To tackle this issue, we initially employ the feature activation differences between clean and adversarial examples to analyze the underlying causes of CO. Intriguingly, our findings reveal that CO can be attributed to the feature coverage induced by a few specific pathways.
Separable Multi-Concept Erasure from Diffusion Models
Large-scale diffusion models, known for their impressive image generation capabilities, have raised concerns among researchers regarding social impacts, such as the imitation of copyrighted artistic styles.
EipFormer: Emphasizing Instance Positions in 3D Instance Segmentation
It enhances the initial instance positions through weighted farthest point sampling and further refines the instance positions and proposals using aggregation averaging and center matching.
Referring Image Segmentation Using Text Supervision
Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions.
Fast Adversarial Training with Smooth Convergence
To address this, we analyze the training process of prior FAT work and observe that catastrophic overfitting is accompanied by the appearance of loss convergence outliers.
Temporal Knowledge Graph Reasoning Triggered by Memories
Specifically, the transient learning network considers transient memories as a static knowledge graph, and the time-aware recurrent evolution network learns representations through a sequence of recurrent evolution units from long-short-term memories.
