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简介:

传统机器学习:

  • 感知机
  • 逻辑回归
  • 线性回归

多层感知机:

  • dropout多层感知机
  • 归一化与多层感知机
  • 反向传播与多层感知机

卷积神经网络:

  • 基础
  • 全连接层
  • LeNet
  • AlexNet
  • VGG
  • DenseNet
  • ResNet
  • 归一化层

自编码器

GANs

GNNs

RNNs

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最新论文

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face. We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using this module, we extend the architecture of an identity-specific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art methods. The code is already available on github: https://github.com/neuralchen/SimSwap.

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