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cs501r_f2016:lab13

Objective:

To explore an alternative use of DNNs by implementing the style transfer algorithm.


Deliverable:

For this lab, you will need to implement the style transfer algorithm of Gatys et al.

  1. You must extract statistics from the content and style images
  2. You must formulate an optimization problem over an input image
  3. You must optimize the image to match both style and content

You should turn in the following:

  1. The final image that you generated
  2. Your code

An example image that I generated is shown at the right.


Grading standards:

Your code will be graded on the following:

  • 35% Correct extraction of statistics
  • 35% Correct construction of cost function
  • 20% Correct initialization and optimization of image variable
  • 10% Awesome looking final image

Description:

For this lab, you should implement the style transfer algorithm referenced above. We are providing the following:

In the scaffolding code, you will find some examples of how to use the provided VGG model. (This model is a slightly modified version of code available here).

Note: In class, we discussed how to construct a computation graph that reuses the VGG network 3 times (one for content, style, and optimization images). It turns out that you don't need to do that. In fact, we merely need to evaluate the VGG network on the content and style images, and save the resulting activations.

The activations can be used to construct a cost function directly. In other words, we don't need to keep around the content/style VGG networks, because we'll never back-propagate through them.

The steps for completion of this lab are:

  1. Run the VGG network on the content and style images. Save the activations.
  2. Construct a content loss function, based on the paper
  3. Construct a style loss function, based on the paper
    1. For each layer specified in the paper (also noted in the code), you'll need to construct a Gram matrix
    2. That Gram matrix should match an equivalent Gram matrix computed on the style activations
  4. Construct an Adam optimizer, step size 0.1
  5. Initialize all of your variables and reload your VGG weights
  6. Initialize your optimization image to be the content image (or another image of your choosing)
  7. Optimize!

Some of these steps are already done in the scaffolding code.

Note that I ran my DNN for about 6000 steps to generate the image shown above.

Here was my loss function over time:

ITER    LOSS            STYLE LOSS      CONTENT LOSS
0       210537.875000   210537872.00000 0.000000
100     73993.000000    67282552.000000 6710.441406
200     47634.054688    39536856.000000 8097.196777
300     36499.234375    28016930.000000 8482.302734
400     30405.132812    21805504.000000 8599.625977
500     26572.333984    17947418.000000 8624.916016
600     23952.351562    15339518.000000 8612.833008
700     22057.589844    13475838.000000 8581.751953
800     20623.390625    12093137.000000 8530.253906
900     19504.234375    11023667.000000 8480.566406
1000    18598.349609    10174618.000000 8423.731445
1100    17857.289062    9491233.000000  8366.055664
1200    17243.207031    8932358.000000  8310.849609
1300    16727.312500    8470261.000000  8257.049805
1400    16287.441406    8079912.500000  8207.528320
1500    15904.160156    7747010.500000  8157.148926
1600    15567.595703    7453235.500000  8114.359863
1700    15269.226562    7199946.500000  8069.279297
1800    15003.159180    6973264.000000  8029.895020
1900    14762.021484    6776666.500000  7985.354492
2000    14544.566406    6602410.000000  7942.156738
2100    14347.167969    6442019.000000  7905.148926
2200    14166.757812    6299105.500000  7867.651367
2300    13999.201172    6169558.500000  7829.643066
2400    13845.177734    6053753.000000  7791.424316
2500    13701.140625    5946503.500000  7754.636230
2600    13566.027344    5846906.000000  7719.121582
2700    13440.531250    5751874.500000  7688.655762
2800    13322.011719    5664197.500000  7657.814453
2900    13210.117188    5585183.000000  7624.934570
3000    13105.109375    5510268.000000  7594.841797
3100    13005.414062    5440027.500000  7565.385742
3200    12912.160156    5376126.000000  7536.033203
3300    12824.537109    5316451.500000  7508.085938
3400    12742.234375    5259337.500000  7482.895996
3500    12663.185547    5202367.500000  7460.817871
3600    12588.695312    5151772.000000  7436.922363
3700    12517.728516    5103315.000000  7414.413574
3800    12450.191406    5055678.000000  7394.513184
3900    12385.476562    5012455.000000  7373.021484
4000    12323.820312    4973657.000000  7350.163086
4100    12263.249023    4937481.000000  7325.767578
4200    12204.673828    4898750.000000  7305.923340
4300    12148.785156    4860086.000000  7288.698242
4400    12095.140625    4822883.500000  7272.257324
4500    12043.544922    4787642.500000  7255.902832
4600    11992.242188    4753499.500000  7238.742188
4700    11942.533203    4722825.500000  7219.708008
4800    11895.559570    4695372.500000  7200.187012
4900    11849.578125    4666181.000000  7183.397461
5000    11804.967773    4639222.500000  7165.745117
5100    11762.816406    4614679.500000  7148.136719
5200    11722.379883    4589744.000000  7132.635742
5300    11682.291016    4565345.000000  7116.945312
5400    11642.744141    4541704.500000  7101.039062
5500    11604.595703    4519445.000000  7085.149902
5600    11568.400391    4497892.000000  7070.507812
5700    11533.195312    4478154.000000  7055.040527
5800    11497.519531    4459191.000000  7038.328125
5900    11463.125977    4439539.000000  7023.586914
6000    11429.999023    4421518.000000  7008.480957

Hints:

You should make sure that if you initialize your image to the content image, and your loss function is strictly the content loss, that your loss is 0.0

I found that it was important to clip pixel values to be in [0,255]. To do that, every 100 iterations I extracted the image, clipped it, and then assigned it back in.

cs501r_f2016/lab13.txt · Last modified: 2016/11/22 13:42 by wingated