DLCV - Intro to Neural Nets
Clustering
一次給你 n 筆資料,把這 n 筆資料分成 k 堆
High within-cluster (intra-cluster) similari
- 同一堆內的影像越像越好
Low between-cluster (inter-cluster) similarit
But Similarity is NOT Always Objective
Similarity
K-Means Clustering
- Input: N examples {x1, . . . , xN } (xn ∈ RD ); number of partitions K
- Initialize: K cluster centers μ1, . . . , μK . Several initialization options:
- Randomly initialize μ1, . . . , μK anywhere in RD
- Or, simply choose any K examples as the cluster centers
- Iterate:
- Assign each of example xn to its closest cluster center
- Recompute the new cluster centers μk (mean/centroid of the set Ck )
- Repeat while not converge
- Possible convergence criteria:
- Cluster centers do not change anymore
- Max. number of iterations reached
- Output:
- K clusters (with centers/means of each clust
L2 可能會出現的問題..
因此他 Sensitive to initialization
- Limitations
- Sensitive to initialization → multiple trials -> majority votes
- Sensitive to outliers → L2 -> L1
- Hard assignment only → fuzzy k-means, etc
soft assignment
- 每個點可以被分配到多個不同類別,且分配基於概率或權重
- 軟分配讓每個點以不同程度屬於不同的類別或群,不僅僅只是一個類別
Linear Classifier
- Consider that we have 10 object categories of interest
- E.g., CIFAR10 with 50K training & 10K test images of 10 categories. And, each image is of size 32 x 32 x 3 pixel
f(x, W):input channel W: classifier x: input img 32 x 32 x 3 = 3072 b: bias32 x 32 -> spatical resol
3 -> RGB channel
可以專注在重要的特徵上,不用 3072 全計算
假設 y4 會期望特別高,y4 is ground truth of y
期望 cat score 特別高
input vector 跟相對應類別狂做內積的結果
得到每個類別模糊的平均行塊
距離和相似度是相反,相對應的概念
算距離不好算,可以算相似度,反之
Loss Function
計算 W 中每個 x_i 和 y_i 的距離 (和標準答案的距離)
- Softmax
Activaction Function
- Sigmoid Function
Training a Single Neuron
可以和 p24~p26 做對照
為了防止 overfitting 會加 regularization