[meta] (paper 14) Siamese Neural Networks for One-shot Image Recognition

Siamese Neural Networks for One-shot Image Recognition (2015)

Contents

1. Abstract
2. Approach
3. Deep Siamese Networks for Image Verification
   1. Model
   2. Learning

0. Abstract

Siamese NN : rank similarity between inputs

• not just to new data
• but also to “new class”

1. Approach

learn image representations, via supervised “metric-based approach”

with “SIAMESE neural network”

& reuse network’s features for one-shot learning

Employ “Siamese CNN”

• 1) capable of learning generic “image feature”
• 2) easily trained using standard optimization techniques
• 3) does NOT rely upon domain-specific knowledge

Pairing with the HIGHEST score ( according to verification network )

\(\rightarrow\) HIGHEST probability for one-shot task

2. Deep Siamese Networks for Image Verification

Siamese Net : 1990s에 처음 소개

• consists of twin networks

This paper

• use weighted \(L_1\) distance between twin feature vectors \(\mathbf{h_1}\) & \(\mathbf{h_2}\)
• sigmoid activation 사용
• cross-entropy objective

2-1. Model

2-2. Learning

(1) Loss Function

Notation

• \(M\) : minibatch size
• \(\mathbf{y}\left(x_{1}^{(i)}, x_{2}^{(i)}\right)\) : length- \(M\) vector which contains the labels for the minibatch
  • \(y\left(x_{1}^{(i)}, x_{2}^{(i)}\right)=1\) : same class
  • \(y\left(x_{1}^{(i)}, x_{2}^{(i)}\right)=0\) : different class

Regularized CE

• \(\begin{gathered} \mathcal{L}\left(x_{1}^{(i)}, x_{2}^{(i)}\right)=\mathbf{y}\left(x_{1}^{(i)}, x_{2}^{(i)}\right) \log \mathbf{p}\left(x_{1}^{(i)}, x_{2}^{(i)}\right)+ \left(1-\mathbf{y}\left(x_{1}^{(i)}, x_{2}^{(i)}\right)\right) \log \left(1-\mathbf{p}\left(x_{1}^{(i)}, x_{2}^{(i)}\right)\right)+\boldsymbol{\lambda}^{T} \mid \mathbf{w} \mid ^{2} \end{gathered}\).