Classic backbone model cheat sheet in CV field (2)

foreword

This article is a continuation of the classic backbone model cheat sheet (1) in the CV field . Also record some interesting backbone models.

An understanding of domain adaptation in transfer learning and an introduction to 3 techniques

Model

DynaMixer

ICML 2022
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
Code: https://github.com/ziyuwwang/DynaMixer , 2022.8.5 Have not seen pre-training weights announced yet.

Block pseudocode, source original paper.

###### initializaiton #######
proj_c = nn.Linear(D, D)
proj_o = nn.Linear(D, D)
###### code in forward ######
def dyna_mixer_block(self, X):
	H, W, D = X.shape
	# row mixing
	for h = 1:H
		Y_h[h,:,:] = DynaMixerOp_h(X[h,:,:])
	# column mixing
	for w = 1:W
		Y_w[:,w,:] = DynaMixerOp_w(X[:,w,:])
	# channel mixing
	Y_c = proj_c(X)
	Y_out = Y_h + Y_w + Y_c
	return proj_o(Y_out)