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DualNet: Learn Complementary Features for Image Recognition
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This repository is for the paper "DualNet: Learn Complementary Features for Image Recognition"

Download Links: [Paper] [Supplementary Material]

./caffe-dualnet: modified from Caffe (
./dualnet-dataset: the prototxt defining the models for each dataset


We illustrate the training process taking DNI on CIFAR100 as an example:

Training standard deep model

./build/tools/caffe.bin train -solver data/pklcifar100/model/v4_ninnet/solver.prototxt 2>&1 | tee -a data/pklcifar100/model/v4_ninnet/pklcifar100_nin_log.txt

Iterative Training (max_iter is set to 1 in this case)

ln -s data/pklcifar100/model/v4_ninnet/snapshot/v4_pklcifar100_nin_iter_120000.caffemodel data/pklcifar100/model/v4_ninnet/pklcifar100_nin_train_iter_120000.caffemodel_coarse
./build/tools/caffe train -solver data/pklcifar100/model/v4_ninnet/res_e1/res_e1_solver.prototxt -weights data/pklcifar100/model/v4_ninnet/pklcifar100_nin_train_iter_120000.caffemodel_coarse 2>&1 | tee -a data/pklcifar100/model/v4_ninnet/res_e1/pklcifar100_nin_res_e1_log.txt

The pretrained models are available at here.


Please cite the following paper if you find this useful in your research:

Title = {DualNet: Learn Complementary Features for Image Recognition},
Author = {Saihui Hou, Xu Liu and Zilei Wang},
Booktitle = {IEEE International Conference on Computer Vision (ICCV)},
Year = {2017}
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