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ShichenLiuShichenLiu
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monir fix
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‎caffe/.gitignore‎

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‎caffe/include/caffe/layers/jmmd_layer.hpp‎

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@@ -60,6 +60,7 @@ class JMMDLossLayer : public LossLayer<Dtype> {
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Blob<Dtype> delta_;
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int label_kernel_num_;
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Dtype label_kernel_mul_;
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int train_iter_num_;
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};
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} // namespace caffe

‎caffe/kmake.sh‎

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cd src
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protoc ./caffe/proto/caffe.proto --cpp_out=../include/
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cd ..
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make -j48

‎caffe/models/DAN/alexnet/train_val.prototxt‎

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@@ -5,7 +5,7 @@ layer {
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top: "source_data"
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top: "lp_labels"
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image_data_param {
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source: "./data/office/webcam_list.txt"
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source: "./data/office/amazon_list.txt"
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batch_size: 64
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shuffle: true
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new_height: 256
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top: "target_data"
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top: "target_label"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 64
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shuffle: true
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new_height: 256
@@ -49,7 +49,7 @@ layer {
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top: "data"
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top: "lp_labels"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 1
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shuffle: false
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new_height: 256

‎caffe/models/JAN/alexnet/solver.prototxt‎

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net: "models/JAN/alexnet/train_val.prototxt"
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test_iter: 2817
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test_iter: 795 # target domain: amazon 2817, webcam 795, dslr 498
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test_interval: 500
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base_lr: 0.001
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lr_policy: "inv"
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snapshot: 60000
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snapshot_prefix: "models/JAN/alexnet/trained_model"
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snapshot_after_train: false
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solver_mode: GPU
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solver_mode: GPU
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‎caffe/models/JAN/alexnet/train_val.prototxt‎

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@@ -5,15 +5,15 @@ layer {
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top: "source_data"
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top: "source_label"
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image_data_param {
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source: "./data/office/webcam_list.txt"
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source: "./data/office/amazon_list.txt"
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batch_size: 64
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new_height: 256
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new_width: 256
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shuffle: true
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}
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transform_param {
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crop_size: 227
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mirror: true
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mirror: true
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mean_file: "./data/ilsvrc12/imagenet_mean.binaryproto"
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}
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include: { phase: TRAIN }
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top: "target_data"
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top: "target_label"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 64
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new_height: 256
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new_width: 256
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top: "data"
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top: "label"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 1
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new_height: 256
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new_width: 256
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type: "InnerProduct"
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bottom: "fc7"
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top: "fc8"
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# blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
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# blobs_lr is set higher than at other layers, as this layer is trained from random weights while the others are fine-tuned
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param {
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lr_mult: 1
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decay_mult: 1
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}
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include: { phase: TRAIN }
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}
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layer {
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name: "silence"
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type: "Silence"
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bottom: "fc8_target"
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include: { phase: TRAIN }
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}
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layer {
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name: "softmax_loss"
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type: "SoftmaxWithLoss"
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top: "softmax_loss"
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include: { phase: TRAIN }
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}
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layer {
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name: "silence_fc8_target"
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type: "Silence"
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bottom: "fc8_target"
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include: { phase: TRAIN }
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}
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layer {
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name: "fc8_softmax"
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type: "Softmax"
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bottom: "fc8"
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top: "fc8_softmax"
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include: { phase: TRAIN }
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}
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layer {
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name: "slice_fc7"
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type: "Slice"
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bottom: "fc7"
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top: "fc7_source"
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top: "fc7_target"
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slice_param {
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slice_dim: 0
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}
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include: { phase: TRAIN }
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}
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layer {
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name: "slice_softmax"
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type: "Slice"
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layer {
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name: "jmmd_loss"
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type: "JMMDLoss"
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bottom: "fc7_source"
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bottom: "fc7_target"
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bottom: "source_softmax"
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bottom: "target_softmax"
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loss_weight: 0.3 #best 0.3 for our tasks, can be tuned within [0.1, 1.0]
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bottom: "source_softmax"
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bottom: "target_softmax"
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loss_weight: 0.3 # best 0.3 for our tasks, can be tuned within [0.1, 1.0]
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top: "jmmd_loss"
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include: { phase: TRAIN }
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}
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layer {
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name: "silence_loss_value"
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name: "silence_jmmd_loss"
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type: "Silence"
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bottom: "jmmd_loss"
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include: { phase: TRAIN }
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# we don't calculate jmmd loss value in our code
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}
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#The following three layers could be added for better performance
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#layer {
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# name: "slice_fc7"
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# type: "Slice"
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# bottom: "fc7"
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# top: "fc7_source"
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# top: "fc7_target"
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# slice_param {
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# slice_dim: 0
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# }
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# include: { phase: TRAIN }
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#}
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#layer {
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# name: "jmmd_loss_fc7"
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# type: "JMMDLoss"
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# bottom: "fc7_source"
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# bottom: "fc7_target"
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# bottom: "source_softmax"
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# bottom: "target_softmax"
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# loss_weight: 0.3 # best 0.3 for our tasks, can be tuned within [0.1, 1.0]# top: "jmmd_loss_fc7"
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# top: "jmmd_loss_fc7"
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# include: { phase: TRAIN }
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#}
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#layer {
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# name: "silence_jmmd_loss_fc7"
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# type: "Silence"
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# bottom: "jmmd_loss_fc7"
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# include: { phase: TRAIN }
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#}

‎caffe/models/JAN/resnet/solver.prototxt‎

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net: "models/JAN/resnet/train_val.prototxt"
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test_iter: 2817
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test_iter: 795 # target domain: amazon 2817, webcam 795, dslr 498
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test_interval: 500
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base_lr: 0.0003
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lr_policy: "inv"

‎caffe/models/JAN/resnet/train_val.prototxt‎

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top: "source_data"
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top: "source_label"
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image_data_param {
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source: "./data/office/webcam_list.txt"
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source: "./data/office/amazon_list.txt"
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batch_size: 16
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new_height: 224
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new_width: 224
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top: "target_data"
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top: "target_label"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 16
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new_height: 224
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new_width: 224
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top: "data"
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top: "label"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 1
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new_height: 224
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new_width: 224
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type: "InnerProduct"
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bottom: "bottleneck"
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top: "fc8"
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# blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
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# blobs_lr is set higher than for other layers, as this layer is trained from random weights while the others are fine-tuned
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param {
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lr_mult: 1
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decay_mult: 1
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bottom: "bottleneck_target"
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bottom: "source_softmax"
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bottom: "target_softmax"
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loss_weight: 0.3 #best 0.3 for our tasks, can be tuned within [0.1, 1.0]
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loss_weight: 0.3 # best 0.3 for our tasks, can be tuned within [0.1, 1.0]
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top: "jmmd_loss"
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include: { phase: TRAIN }
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}
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type: "Silence"
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bottom: "jmmd_loss"
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include: { phase: TRAIN }
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# we don't calculate jmmd loss value in our code
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}

‎caffe/models/RTN/alexnet/solver.prototxt‎

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net: "./models/RTN/alexnet/train_val.prototxt"
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test_iter: 2817
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test_iter: 795 # target domain: amazon 2817, webcam 795, dslr 498
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test_interval: 500
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base_lr: 0.001
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momentum: 0.9

‎caffe/models/RTN/alexnet/train_val.prototxt‎

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name: "outer_product_amazon_to_webcam"
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name: "amazon_to_webcam"
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layer {
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name: "source_data"
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type: "ImageData"
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top: "source_data"
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top: "lp_labels"
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image_data_param {
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source: "./data/office/webcam_list.txt"
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source: "./data/office/amazon_list.txt"
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batch_size: 64
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shuffle: true
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new_height: 256
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top: "target_data"
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top: "target_label"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 64
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shuffle: true
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new_height: 256
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top: "data"
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top: "lp_labels"
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image_data_param {
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source: "./data/office/amazon_list.txt"
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source: "./data/office/webcam_list.txt"
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batch_size: 1
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shuffle: true
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new_height: 256

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