CNN as Fixed Feature Extractor
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Deep Learning & Generative AI
Convolution Neural Networks
Interview questions on Convolution Neural Networks.
27 questions
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CNN as Fixed Feature Extractor
Q2. **True or False**: Unlike CNN architectures such as AlexNet or VGG, ResNet does not have any hidden FC layers?
CNN as Fixed Feature Extractor
Q3. Assuming the VGG-Net has 138, 357, 544 floating point parameters, what is the physical size in Mega-Bytes (MB) required for persisting a trained instance of VGG-Net on permanent storage?
CNN as Fixed Feature Extractor
Q4. **True or False**: Most attempts at researching image representation using FE, focused solely on reusing the activations obtained from layers close to the output of the CNN, and more specifically the fully-connected layers.
CNN as Fixed Feature Extractor
Q5. **True or False**: FE in the context of deep learning is particularly useful when the target problem does not include enough labeled data to successfully train CNN that generalizes well.
CNN as Fixed Feature Extractor
Q6. Why is a CNN trained on the ImageNet dataset a good candidate for a source problem?
CNN as Fixed Feature Extractor
Q7. Complete the missing parts regarding the VGG19 CNN architecture: 1. The VGG19 CNN consists of [...] layers. 2. It consists of [...] convolutional and 3 [...] layers. 3. The input image size is [...]. 4. The number of input channels is [...]. 5. Every image has it’s mean RGB value [subtracted / added]. 6. Each convolutional layer has a [small/large] kernel sized [...]. 7. The number of pixels for padding and stride is [...]. 8. There are 5 [...] layers having a kernel size of [...] and a stride of [...] pixels. 9. For non-linearity a [rectified linear unit (ReLU [5])/sigmoid] is used. 10. The [...] FC layers are part of the linear classifier. 11. The first two FC layers consist of [...] features. 12. The last FC layer has only [...] features. 13. The last FC layer is terminated by a [...] activation layer. 14. Dropout [is / is not] being used between the FC layers.
CNN as Fixed Feature Extractor
Q8. The following question discusses the method of fixed feature extraction from layers of the VGG19 architecture for the classification of pancreatic cancer. It depicts FE principles which are applicable with minor modifications to other CNNs as well. Therefore, if you happen to encounter a similar question in a job interview, you are likely be able to cope with it by utilizing the same logic. In Fig. (9.7) three different classes of pancreatic cancer are displayed: A, B and C, curated from a dataset of 4K Whole Slide Images (WSI) labeled by a board certified pathologist. Your task is to use FE to correctly classify the images in the dataset. <table align='center'> <tr> <td align="center"> <img src="img/cnn_feature-1.png" alt= "A dataset of 4K histopathology WSI from three severity classes: A, B and C" style="max-width:70%;" /> </td> </tr> <tr> <td align="center">A dataset of 4K histopathology WSI from three severity classes: A, B and C </td> </tr> </table> Table (9.3) presents an incomplete listing of the of the VGG19 architecture. As depicted, for each layer the number of filters (i. e., neurons with unique set of parameters), learnable parameters (weights,biases), and FV size are presented. <table align='center'> <tr> <td align="center"> <img src="img/cnn_feature-2.png" alt= "Incomplete listing of the VGG19 architecture" style="max-width:70%;" /> </td> </tr> <tr> <td align="center">Incomplete listing of the VGG19 architecture </td> </tr> </table> 1. Describe how the VGG19 CNN may be used as fixed FE for a classification task. In your answer be as detailed as possible regarding the stages of FE and the method used for classification. 2. Referring to Table (9.3), suggest three different ways in which features can be extrac- ted from a trained VGG19 CNN model. In each case, state the extracted feature layer name and the size of the resulting FE. 3. After successfully extracting the features for the 4K images from the dataset, how can you now classify the images into their respective categories?
CNN as Fixed Feature Extractor
Q9. Still referring to Table (9.3), a data scientist suggests using the output layer of the VGG19 CNN as a fixed FE. What is the main advantage of using this layer over using for instance, the fc7 layer? (Hint: think about an ensemble of feature extractors)
CNN as Fixed Feature Extractor
Q10. Still referring to Table (9.3) and also to the code snippet in Fig.(7.4), which represents a new CNN derived from the VGG19 CNN: ```python import torchvision.models as models ... class VGG19FE(torch.nn.Module): def __init__(self): super(VGG19FE, self).__init__() original_model = models.VGG19(pretrained=[???]) self.real_name = (((type(original_model).__name__))) self.real_name = "vgg19" self.features = [???] self.classifier = torch.nn.Sequential([???]) self.num_feats = [???] def forward(self, x): f = self.features(x) f = f.view(f.size(0), -1) f = [???] print (f.data.size()) return f ``` 1. Complete line 6; what should be the value of `pretrained` ? 2. Complete line 10; what should be the value of `self.features` ? 3. Complete line 12; what should be the value of `self.num_feats` ? 4. Complete line 17; what should be the value of `f` ?
CNN as Fixed Feature Extractor
Q11. We are still referring to Table (9.3) and using the skeleton code provided in Fig. (7.5) to derive a new CNN entitled `ResNetBottom` from the ResNet34 CNN, to extract a 512- dimensional FV for a given input image. Complete the code as follows: ```python import torchvision.models as models res_model = models.resnet34(pretrained=True) class ResNetBottom(torch.nn.Module): def __init__(self, original_model): super(ResNetBottom, self).__init__() self.features = [???] def forward(self, x): x = [???] x = x.view(x.size(0), -1) return x ``` 1. The value of self.features in line 7. 2. The forward method in line 11.
CNN as Fixed Feature Extractor
Q12. Still referring to Table (9.3), the PyTorch based pseudo code snippet in Fig. (7.6) returns the 512-dimensional FV from the modified ResNet34 CNN, given a 3-channel RGB image as an input. ```python import torchvision.models as models from torchvision import transforms ... test_trans = transforms.Compose([ transforms.Resize(imgnet_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) def ResNet34FE(image, model): f=None image = test_trans(image) image = Variable(image, requires_grad=False).cuda() image= image.cuda() f = model(image) f = f.view(f.size(1), -1) print ("Size : {}".format(f.shape)) f = f.view(f.size(1),-1) print ("Size : {}".format(f.shape)) f =f.cpu().detach().numpy()[0] print ("Size : {}".format(f.shape)) return f ``` *PyTorch code skeleton for extracting a 512-dimensional FV from a pre-trained ResNet34 CNN model.* Answer the following questions regarding the code in Fig. (7.6): 1. What is the purpose of `test_trans` in line 5? 2. Why is the parameter `requires_grad` set to False in line 14? 3. What is the purpose of `f.cpu()` in line 23? 4. What is the purpose of `detach()` in line 23? 5. What is the purpose of `numpy()[0]` in line 23?
CNN as Fixed Feature Extractor
Q13. Define the term fine-tuning (FT) of an ImageNet pre-trained CNN.
CNN as Fixed Feature Extractor
Q14. Describe three different methods by which one can fine-tune an ImageNet pre-trained CNN.
CNN as Fixed Feature Extractor
Q15. Melanoma is a lethal form of malignant skin cancer, frequently misdiagnosed as a benign skin lesion or even left completely undiagnosed. In the United States alone, melanoma accounts for an estimated 6, 750 deaths per annum. With a 5-year survival rate of 98%, early diagnosis and treatment is now more likely and possibly the most suitable means for melanoma related death reduction. Dermoscopy images, shown in Fig. (7.7) are widely used in the detection and diagnosis of skin lesions. Dermatologists, relying on personal experience, are involved in a laborious task of manually searching dermoscopy images for lesions. Therefore, there is a very real need for automated analysis tools, providing assistance to clinicians screening for skin metastases. In this question, you are tasked with addressing some of the fundamental issues DL researchers face when building deep learning pipelines. As suggested in, you are going to use ImageNet pre-trained CNN to resolve a classification task. <table align='center'> <tr> <td align="center"> <img src="img/cnn_feature-3.png" alt= "Skin lesion categories. An exemplary visualization of melanoma" style="max-width:70%;" /> </td> </tr> <tr> <td align="center">Skin lesion categories. An exemplary visualization of melanoma</td> </tr> </table> 1. Given that the skin lesions fall into seven distinct categories, and you are training using cross-entropy loss, how should the classes be represented so that a typical PyTorch training loop will successfully converge? 2. Suggest several data augmentation techniques to augment the data. 3. Write a code snippet in PyTorch to adapt the CNN so that it can predict 7 classes instead of the original source size of 1000. 4. In order to fine tune our CNN, the (original) output layer with $1000$ classes was removed and the CNN was adjusted so that the (new) classification layer comprised seven softmax neurons emitting posterior probabilities of class membership for each lesion type.
CNN as Fixed Feature Extractor
Q16. For neural networks that work with images like VGG-19, InceptionNet, you often see a visualization of what type of features each filter captures. How are these visualizations created?
CNN as Fixed Feature Extractor
Q17. Filter size. 1. How are your model’s accuracy and computational efficiency affected when you decrease or increase its filter size? 1. How do you choose the ideal filter size?
CNN as Fixed Feature Extractor
Q18. Convolutional layers are also known as “locally connected.” Explain what it means.
CNN as Fixed Feature Extractor
Q19. When we use CNNs for text data, what would the number of channels be for the first conv layer?
CNN as Fixed Feature Extractor
Q20. What is the role of zero padding?
CNN as Fixed Feature Extractor
Q21. Why do we need upsampling? How to do it?
CNN as Fixed Feature Extractor
Q22. What does a 1x1 convolutional layer do?
CNN as Fixed Feature Extractor
Q23. Pooling. 1. What happens when you use max-pooling instead of average pooling? 1. When should we use one instead of the other? 1. What happens when pooling is removed completely? 1. What happens if we replace a 2 x 2 max pool layer with a conv layer of stride 2?
CNN as Fixed Feature Extractor
Q24. When we replace a normal convolutional layer with a depth wise separable convolutional layer, the number of parameters can go down. How does this happen? Give an example to illustrate this.
CNN as Fixed Feature Extractor
Q25. Can you use a base model trained on ImageNet (image size 256 x 256) for an object classification task on images of size 320 x 360? How?
CNN as Fixed Feature Extractor
Q26. How can a fully-connected layer be converted to a convolutional layer?
CNN as Fixed Feature Extractor