First, our proposed PPT model introduces FocalNCE loss in patch-wise bidirectional contrastive learning to ensure high consistency between input and output images. Second, we propose a novel patch alignment loss to address the commonly observed misalignment issue in paired medical image datasets. Third...
Paper tables with annotated results for ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
首先,作者定义了CIC( (Increase of Confidence)的概念,既相对于baseline图片的置信度增量 对获取的特征图进行channel-wise遍历,对每层特征图进行上采样+归一化,与原始图片进行pixel-wise相乘融合,然后送进网络获取目标类别score(softmax后),减去baseline的目标类别score,获取CIC。再进行softmax操作来保证所有CIC之和为1...
The experiment results suggest that (1) POPAR outperforms state-of-the-art (SoTA) self-supervised models with vision transformer backbone; (2) POPAR achieves significantly better performance over all three SoTA contrastive learning methods; and (3) POPAR also outperforms fully-supervised pretrained...