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Common Applications
Application | Description |
🧒👧 Face Verification | Recognizing if that the given image and ID are belonging to the same person |
👸 Face Recognition | Assigning ID to the input face image |
🌠 Neural Style Transfer | Converting an image to another by learning the style from a specific image |
Term | Question | Input | Output | Problem Class |
🧒👧 Face Verification | Is this the claimed person? 🕵️♂️ | Face image / ID | True / False | 1:1 |
👸 Face Recognition | Who is this person? 🧐 | Face image | ID of K faces in DB | 1:K |
Learning from one example (that we have in the database) to recognize the person again
- Get input image
- Check if it belongs to the faces you have in the DB
We have to calculate the similarity between the input image and the image in the database, so:
- ⭕ Use some function that
- similarity(img_in, img_db) = some_val
- 👷♀️ Specifiy a threshold value
- 🕵️♀️ Check the threshold and specify the output
A CNN which is used in face verification context, it recievs two images as input, after applying convolutions it calculates a feature vector from each image and, calculates the difference between them and then gives outputs decision.
In other words: it encodes the given images
Architecture:
We can train the network by taking an anchor (basic) image A and comparing it with both a positive sample P and a negative sample N. So that:
- 🚧 The dissimilarity between the anchor image and positive image must low
- 🚧 The dissimilarity between the anchor image and the negative image must be high
So:
Another variable called margin, which is a hyperparameter is added to the loss equation. Margin defines how far away the dissimilarities should be, i.e if margin = 0.2 and d(a,p) = 0.5 then d(a,n) should at least be equal to 0.7. Margin helps us distinguish the two images better 🤸♀️
Therefore, by using this loss function we:
- 👩🏫 Calculate the gradients and with the help of the gradients
- 👩🔧 We update the weights and biases of the Siamese network.
For training the network, we:
- 👩🏫 Take an anchor image and randomly sample positive and negative images and compute its loss function
- 🤹♂️ Update its gradients
Generating an image G by giving a content image C and a style image S
So to generate G, our NN has to learn features from S and apply suitable filters on C
Usually we optimize the parameters -weights and biases- of the NN to get the wanted performance, here in Neural Style Transfer we start from a blank image composed of random pixel values, and we optimize a cost function by changing the pixel values of the image 🧐
In other words, we:
- ⭕ Start with a blank image consists of random pixels
- 👩🏫 Define some cost function J
- 👩🔧 Iteratively modify each pixel so as to minimize our cost function
Long story short: While training NNs we update our weights and biases, but in style transfer, we keep the weights and biases constant, and instead update our image itself 🙌
We can define J as
Which:
- denotes the similarity between G and C
- denotes the similarity between G and S
- α and β hyperparameters
Last modified 2yr ago