cs501r_f2016:lab9
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cs501r_f2016:lab9 [2021/06/30 23:42] (current) |
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| - You must implement a siamese network that accepts two input images | - You must implement a siamese network that accepts two input images | ||
| - The network must output the probability that the two images are the same class | - The network must output the probability that the two images are the same class | ||
| + | - You should train the network using the contrastive loss function discussed in class | ||
| - Your implementation should use a ResNet architecture | - Your implementation should use a ResNet architecture | ||
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| - A tensorboard screenshot showing that your architecture is, indeed, a siamese architecture | - A tensorboard screenshot showing that your architecture is, indeed, a siamese architecture | ||
| - Your code | - Your code | ||
| - | - A small writeup (1/2 - 1 page) describing your test/training split, your resnet architecture, and the final performance of your classifier. | + | - A small writeup (<1 page) describing your test/training split, your resnet architecture, and the final performance of your classifier. |
| You should use the [[http://www.openu.ac.il/home/hassner/data/lfwa/|Labeled Faces in the Wild-a]] dataset (also available for | You should use the [[http://www.openu.ac.il/home/hassner/data/lfwa/|Labeled Faces in the Wild-a]] dataset (also available for | ||
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| - Make sure that parameters are shared across both halves of the network! | - Make sure that parameters are shared across both halves of the network! | ||
| - Train the network using an optimizer of your choice | - Train the network using an optimizer of your choice | ||
| + | - You should use some sort of SGD. | ||
| + | - You will need to sample same/different pairs. | ||
| Note: you will NOT be graded on the accuracy of your final classifier, as long as you make a good faith effort to come up with something that performs reasonably well. | Note: you will NOT be graded on the accuracy of your final classifier, as long as you make a good faith effort to come up with something that performs reasonably well. | ||
| Your ResNet should extract a vector of features from each image. Those feature vectors should then be compared to calculate an "energy"; that energy should then be input into a contrastive loss function, as discussed in class. | Your ResNet should extract a vector of features from each image. Those feature vectors should then be compared to calculate an "energy"; that energy should then be input into a contrastive loss function, as discussed in class. | ||
| + | |||
| + | Remember that your network should be symmetric, so if you swap input images, nothing should change. | ||
| Note that some people in the database only have one image. These images are still useful, however (why?), so don't just throw them away. | Note that some people in the database only have one image. These images are still useful, however (why?), so don't just throw them away. | ||
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| - The final performance of your classifier | - The final performance of your classifier | ||
| - | This writeup should be small - between 1/2 - 1 page. You don't need to wax eloquent. | + | This writeup should be small - less than 1 page. You don't need to wax eloquent. |
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