cs501r_f2016:lab9
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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. | ||
cs501r_f2016/lab9.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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