An autoencoder can only capture linear relationship between features True False
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- Matplotlib code outputLaplacian Image Pyramid A Laplacian image pyramid is a multi-scale representation of a single image that highlights intensity discontinuities at multiple scales. It is obtained by convolving an image with a Gaussian mask and subtracting the smoothed image from the original one. The next level of the pyramid is obtain by repeating this process on the smoothed image after it is down-sampled. Create a 3 level Laplacian image pyramid of the woods image (after conversion from uint8 to double format), using a Gaussian with a standard deviation of 1.5, using "same" as the shape parameter for the conv2 function, and resizing by a scale factor of 0.5 using nearest-neighbour interpolation. Display the images in the pyramid as subplots in the same window.Laplacian Image Pyramid A Laplacian image pyramid is a multi-scale representation of a single image that highlights intensity discontinuities at multiple scales. It is obtained by convolving an image with a Gaussian mask and subtracting the smoothed image from the original one. The next level of the pyramid is obtain by repeating this process on the smoothed image after it is down-sampled. Create a 3 level Laplacian image pyramid of the woods image (after conversion from uint8 to double format), using a Gaussian with a standard deviation of 1.5, using "same" as the shape parameter for the conv2 function, and resizing by a scale factor of 0.5 using nearest-neighbour interpolation. Display the images in the pyramid as subplots in the same window. EXERCISE: For the last level of the pyramid report the values at the following locations (correct to at least 2 decimal places): row=3, column=5 row=1, column=2
- With respect to Series object S what will be the output of S[0:4]? (a)Elements from 0 to 3 will be selected (b)Elements from 1 to 4 will be selected (c)Elements from 0 to 4 will be selected (d)Elements from 1 to 3 will be selectedGiven a 2D point (x, y) in screen space with an associated z-buffer value (zbuf), write out psuedocode for determining whether or not to draw that point.Expaination
- Coding as transformation COLLAPSE Present a coding technnique that accepts 2 numbers x,y that will result into a transformation L: R² -> R2. Ask your classmate to respond an input and reply with the transformatic Example: L(x,y) = (x-y,x+y) Sample input: (3,5) Sample Output: (-2,8)IMG_SHAPE = (3, 32, 32) test_input = torch.randn(1, *IMG_SHAPE) model = DeepAutoencoder(IMG_SHAPE, 15) code = model.encode(test_input) deep_autoencoder = DeepAutoencoder((3, 32, 32), 32) deep_autoencoder.load_state_dict(torch.load('deep_autoencoder.pt', map_location='cpu')) for p in deep_autoencoder.parameters(): p.requires_grad_(False) images = X_train # Encode all images ### YOUR CODE HERE ### assert len(codes) == len(images)In computer graphics, what are the characteristics of linear transformation to be noted?