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Table 1 Gradient estimation model architecture

From: Video person reidentification based on neural ordinary differential equations and graph convolution network

The module Gradient estimation model
Subsampling module (optional) [Normalized layer, 3×3 convolutional layer, ReLU layer]×1
[Normalized layer, 4×4 convolutional layer (step size 2), ReLU layer]×2
The ODE module [Normalized layer, ReLU layer, 3×3 convolutional layer]×2
[Normalized layer]×1
Convolution model
Tacit module
[Linear transformation layer, ReLU layer]×3
Fully connected module [Normalized layer, ReLU layer, global maximum pooling layer, linear transformation layer]
  1. Note: the step size of unmarked convolution layer is 1 by default. The number after the multiplication symbol in brackets indicates the number of times the submodule in brackets is repeated