原创 A Content Transformation Block for Image Style Transfer (CVPR2019, 風格遷移)

Kotovenko, Dmytro, et al. “A Content Transformation Block for Image Style Transfer.” CVPR 2019. 風格遷移問題: X→YX \right

原创 Unsupervised pixel-level domain adaptation with generative adversarial networks (DA+ 圖像轉換)

paper: Bousmalis, Konstantinos, et al. “Unsupervised pixel-level domain adaptation with generative adversarial net

原创 Discriminative deep metric learning for face verification in the wild 度量學習(CVPR2014)

Hu, Junlin, Jiwen Lu, and Yap-Peng Tan. “Discriminative deep metric learning for face verification in the wild.” CV

原创 馬氏距離

1. 協方差矩陣是半正定矩陣 對於向量xxx,設其均值爲uuu。那麼協方差矩陣Σ=E[(x−u)(x−u)T]\Sigma=E[(x-u)(x-u)^T]Σ=E[(x−u)(x−u)T] yTΣy=yTE[(x−u)(x−u)T]

原创 Beyond sharing weights for deep domain adaptation (PAMI 2018) ---Transfer Learning

Rozantsev, Artem, Mathieu Salzmann, and Pascal Fua. “Beyond sharing weights for deep domain adaptation.” IEEE trans

原创 Deep Domain Confusion: Maximizing for Domain Invariance

Tzeng E , Hoffman J , Zhang N , et al. Deep Domain Confusion: Maximizing for Domain Invariance[J]. Computer Science

原创 筆記: Gradient Reversal Layer (unsupervised domain adaptation by backpropagation. ICML 2015)

paper: Ganin, Yaroslav, and Victor Lempitsky. “Unsupervised domain adaptation by backpropagation.” ICML 37. JMLR. o

原创 PyTorch 模型參數和optimizer

網絡參數 ( parameters()和named_parameters()) model爲網絡, 打印層結構: ret=[*model.modules()] for layer in ret: #print(type

原创 pandas讀取csv並繪製散點圖

讀取 loc='checkpoints/mnist_vae2/mu_and_sigma.csv' import pandas as pd import numpy as np X=pd.DataFrame.from_csv(loc)

原创 frp內網穿透實例

(目前僅實現了SSH功能) 問題: 具有公網IP的計算機A需要訪問僅有內網IP的gpu服務器C。 情況: 本來想通過具有內網和外網ip的跳板機D實現轉發。但不知D做了何種設置,A始終連不上D。 解決方案: 使用了一臺具有公網ip的雲服

原创 原點矩與中心矩

K階原點矩: E(xk)E(x^k)E(xk),1階原點矩是數學期望。 K階中心矩: E(x−E(x))kE(x-E(x))^kE(x−E(x))k,2階中心矩是方差。

原创 多元高斯分佈/多元正態分佈

這些知識,動不動就忘。爲了不白選了一門數學課,還是把他記下來。 f(x)=1(2π)n/2∣C∣12exp{(x−μ)TC−1(x−μ)}f(x)= \frac{1}{ (2\pi)^{n/2} {|C|}^{\frac{1}{2}}

原创 主子式、順序主子式、餘子式、代數餘子式

K階主子式[1]^{[1]}[1] 以3階行列式爲例: [a1a2a3b1b2b3c1c2c3]\begin{bmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3\\

原创 Tensorflow 教程筆記---Custom layers

import tensorflow as tf tf.enable_eager_execution() Layers: common sets of useful operations # In the tf.keras.laye

原创 Tensorflow 教程筆記---Custom training basics

import tensorflow as tf tf.enable_eager_execution() Variable operator x = tf.zeros([10, 10]) x += 2 # This is equi