Systems and processes for metric learning distillation are disclosed herein. In accordance with one example, a method includes, at an electronic device, at an electronic device having one or more processors and memory, receiving a first plurality of vectors from a first model, receiving a second plurality of vectors from a second model, determining a first plurality of vector distances based on the first plurality of vectors, generating a first matrix based on the first plurality of vector distances, determining a second plurality of vector distances based on the second plurality of vectors, generating a second matrix based on the second plurality of vector distances, comparing the first matrix with the second matrix, and adjusting the second model based on the comparison of the first matrix and the second matrix.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a first plurality of vectors from a first model; receiving a second plurality of vectors from a second model; determining a first plurality of vector distances based on the first plurality of vectors; generating a first matrix based on the first plurality of vector distances; determining a second plurality of vector distances based on the second plurality of vectors; generating a second matrix based on the second plurality of vector distances; comparing the first matrix with the second matrix; and adjusting the second model based on the comparison of the first matrix and the second matrix. 2. The electronic device of claim 1, wherein comparing the first matrix with the second matrix comprises comparing the first matrix with the second matrix using a function. 3. The electronic device of claim 2, wherein the function is a differentiable function, a non-differentiable function, or a combination thereof. 4. The electronic device of claim 3, wherein the function is a squared-error function, a KL-divergence function, a Jensen-Shannon divergence function, or a combination thereof. 5. The electronic device of claim 3, wherein the function is optimized. 6. The electronic device of claim 5, wherein the function is optimized using gradient descent. 7. The electronic device of claim 1, wherein the first matrix is a first distribution and the second matrix is a second distribution and wherein comparing the first matrix with the second matrix comprises comparing the first distribution with the second distribution. 8. The electronic device of claim 1, wherein the first model has a first dimensionality and the second model has a second dimensionality different than the first dimensionality. 9. The electronic device of claim 1, wherein the first model and the second model have a same dimensionality. 10. The electronic device of claim 1, wherein the one or more programs further include instructions for: receiving a third plurality of vectors from a third model; determining a third plurality of vector distances based on the third plurality of vectors; generating a third matrix based on the third plurality of vector distances; and combining the first matrix with the third matrix to provide a total matrix; wherein comparing the first matrix with the second matrix comprises comparing the total matrix with the second matrix, and wherein adjusting the second model based on the comparison of the first matrix and the second matrix comprises adjusting the second model based on the comparison of the total matrix and the second matrix. 11. The electronic device of claim 10, wherein combining the first matrix with the third matrix to provide a total matrix comprises weighting at least one of the first matrix or the third matrix. 12. The electronic device of claim 1, wherein generating a first matrix based on the first plurality of vector distances comprises concatenating at least one vector of the first plurality of vectors with another vector. 13. The electronic device of claim 1, wherein determining a first plurality of vector distances based on the first plurality of vectors comprises: determining a first plurality of vector distances using a metric distance function, a non-metric distance function, or a combination thereof. 14. The electronic device of claim 13, wherein the metric distance function is a Euclidian function, a hamming function, a cosine function, a kernel function, or any combination thereof. 15. The electronic device of claim 1, wherein determining a first plurality of vector distances based on the first plurality of vectors comprises determining the first plurality of vector distances using a first distance function and wherein determining a second plurality of vector distances based on the second plurality of vectors comprises determining the second plurality of vector distances using a second distance function different than the first distance function. 16. The electronic device of claim 1, wherein determining a first plurality of vector distances based on the first plurality of vectors includes a distance between each possible pair of vectors of the plurality of vectors. 17. The electronic device of claim 1, wherein the first plurality of vectors is a subset of a fourth plurality of vectors. 18. The electronic device of claim 1, wherein each vector of the first plurality of vectors corresponds to one or more images. 19. The electronic device of claim 1, wherein the first model is at least one of a classification model or a regression model. 20. The electronic device of claim 1, further comprising: after adjusting the second model, providing, to the second model, a first input and a second input; and providing, using the second model, a first vector corresponding to the first input and a second vector corresponding to the second input, wherein a distance between the first vector and the second vector is indicative of a similarity of the first input and the second input. 21. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to: receive a first plurality of vectors from a first model; receive a second plurality of vectors from a second model; determine a first plurality of vector distances based on the first plurality of vectors; generate a first matrix based on the first plurality of vector distances; determine a second plurality of vector distances based on the second plurality of vectors; generate a second matrix based on the second plurality of vector distances; compare the first matrix with the second matrix; and adjust the second model based on the comparison of the first matrix and the second matrix. 22. A method, comprising: at an electronic device having one or more processors: receiving a first plurality of vectors from a first model; receiving a second plurality of vectors from a second model; determining a first plurality of vector distances based on the first plurality of vectors; generating a first matrix based on the first plurality of vector distances; determining a second plurality of vector distances based on the second plurality of vectors; generating a second matrix based on the second plurality of vector distances; comparing the first matrix with the second matrix; and adjusting the second model based on the comparison of the first matrix and the second matrix.