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异常检测Anomaly Detection 将data经过Encoder#xff0c;在经过Decoder#xff0c;根据输入和输出的差距来判断异常图像。training data是100000张人脸照片#xff0c;testing data有大约10000张跟training data相同… 文章目录 TaskBaselineReportQuestion2 Code Link Task
异常检测Anomaly Detection 将data经过Encoder在经过Decoder根据输入和输出的差距来判断异常图像。training data是100000张人脸照片testing data有大约10000张跟training data相同分布的人脸照片(label 0)还有10000张不同分布的照片(anomaly, label 1)每张照片都是(64,64,3).npy file
以训练集的前三张照片为例auto-encoder的输入和输出如下 Baseline
Auto-encoder model一共有五种模型
fcn: fully-connected networkcnn: convolutional networkVAEResnetMulti-encoder autoencoder encoder(fcnfcnfcn)decoder(fcn)encoder(cnncnncnn)decoder(cnn)encoder(fcnfcnconv)decoder(fcn)
通过FCN调节超参数的方式可以轻易的达到strongResnet也是但是Multi-encoder的方式表现并不好也许是我处理方式有问题具体代码可以参考GitHub中的文件
Report
Question2
Train a fully connected autoencoder and adjust at least two different element of the latent representation. Show your model architecture, plot out the original image, the reconstructed images for each adjustment and describe the differences.
import matplotlib.pyplot as plt
# sample train_dataset[random.randint(0,100000)]
sample train_dataset[0]
print(sample shape:{}.format(sample.size()))
sample sample.reshape(1,3,64,64)model.eval()
with torch.no_grad():img sample.cuda()# 只调整fcn中的latent representation的其中两维其他模型都是正常输出if model_type in [res]:output model(img)output decoder(output)print(res output shape:{}.format(output.size()))output output[0] # 第一个重建图像当然只有一个图像if model_type in [fcn]:img img.reshape(img.shape[0], -1)x model.encoder(img)x[0][2] x[0][2]*3output model.decoder(x)print(fcn output shape:{}.format(output.size()))output output.reshape(3,64,64)if model_type in [vae]:output model(img)print(vae output shape:{}.format(output.size()))output output[0][0] # output[0]是重建后的图像output[0][0]重建后的第一个图像if model_type in [cnn]:output model(img)[0]print(output shape:{}.format(output.size()))sample sample.reshape(3,64,64) # 创建画布
fig, axes plt.subplots(nrows1, ncols2, figsize(5, 5))# plt sample image
axes[0].imshow(transforms.ToPILImage()((sample1)/2)) #imshow的输入(H,W,C)
axes[0].axis(off)
axes[0].annotate(sample input, xy(0.5, -0.15), xycoordsaxes fraction,hacenter, vacenter)
# plt output image
axes[1].imshow(transforms.ToPILImage()((output1)/2))
axes[1].axis(off)
axes[1].annotate(sample output, xy(0.5, -0.15), xycoordsaxes fraction,hacenter, vacenter)plt.show()Code Link
具体代码在Github
