c) 使用随机投影ψ降低计算复杂度,保持Johnson-Lindenstrauss几何特性 数学本质:求解最小最大覆盖问题(公式5),确保子集能近似原始特征空间分布 图3 3.3 基于PatchCore的异常检测 利用构建的正常块特征记忆库M,我们通过计算测试图像 x^{test} 的块特征集合 P(x^{test}) = P_{s,p}(φ_j(x^{test
[torch.Tensor, bs x c x w x h] Returns: x: [torch.Tensor, bs * w//stride * h//stride, c, patchsize, patchsize] """ padding = int((self.patchsize - 1) / 2) # 1 unfolder = torch.nn.Unfold( kernel_size=self.patchsize, stride=self.stride, padding=padding, dilation=1 )...
4, 4] source camera projection matrix, for each source view in batch ref_proj: [B, 4, 4] reference camera projection matrix, for each ref view in batch depth_samples: [B, Ndepth, H, W] virtual depth layers Returns: warped_src_fea: [B, C, Ndepth, H, W] features ...
op_type, op_type, EXPRESSION_MAP[method_name])) /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/math_op_patch.py:341: UserWarning: /tmp/tmp3pdqc646.py:21 The behavior of expression A + B has been unified with elementwise_add(X, Y, axis=-1)...
^C 6 测试 单个类别测试:以category=carpet为例。In [13] %cd /home/aistudio/Anomaly.Paddle/ !python eval.py --data_path=/home/aistudio/data/ --category carpet --method=coreset --arch=resnet18 --k=10 /home/aistudio/Anomaly.Paddle Namespace(arch='resnet18', batch_size=1, category=...
GRAD:一种新的无监督异常检测框架,引入生成和重新加权密集对比模式,以卓越的推理速度实现了具有竞争力的异常检测和定位精度,速度高达1251.6 FPS!单位:上海大学, 复旦大学 点击关注 @CVer官方知乎账号,可以第一时间看到最优质、最前沿的CV、AI工作~ GRAD Generating and Reweighting Dense Contrastive Patterns for Unsupe...