merge

materialandmethods.typ

@@ -98,7 +98,8 @@ See @resnet50impl.~@chowdhury2021fewshotimageclassificationjust
 An especially hard task is to generalize from such few samples.
 In typical supervised learning the model sees thousands or millions of samples of the corresponding domain during learning.
 This helps the model to learn the underlying patterns and to generalize well to unseen data.
-In few-shot learning the model has to generalize from just a few samples.#todo[Source?]#todo[Write more about. eg. class distributions]
+In few-shot learning the model has to generalize from just a few samples.#todo[Write more about. eg. class distributions]
+@Goodfellow-et-al-2016
 
 === Softmax
 #todo[Maybe remove this section]
@@ -131,22 +132,23 @@ Cosine similarity is a widely used metric for measuring the similarity between t
 It computes the cosine of the angle between the vectors, offering a measure of their alignment.
 This property makes the cosine similarity particularly effective in scenarios where the
 direction of the vector holds more important information than the magnitude.
+@dataminingbook@analysisrudin
 
 $
   cos(theta) &:= (A dot B) / (||A|| dot ||B||)\
   &= (sum_(i=1)^n  A_i B_i)/ (sqrt(sum_(i=1)^n A_i^2) dot sqrt(sum_(i=1)^n B_i^2))
 $ <cosinesimilarity>
 
-#todo[Source?]
 === Euclidean Distance
 The euclidean distance (@euclideannorm) is a simpler method to measure the distance between two points in a vector space.
 It just calculates the square root of the sum of the squared differences of the coordinates.
 the euclidean distance can also be represented as the L2 norm (euclidean norm) of the difference of the two vectors.
+@analysisrudin
 
 $
   cal(d)(A,B) = ||A-B|| := sqrt(sum_(i=1)^n (A_i - B_i)^2)
 $ <euclideannorm>
-#todo[Source?]
+
 
 === Patchcore
 // https://arxiv.org/pdf/2106.08265