diff --git a/examples/ssl/vgg16_cifar_ssl_channels_training.yaml b/examples/ssl/vgg16_cifar_ssl_channels_training.yaml
new file mode 100755
index 0000000000000000000000000000000000000000..9b7155bfab9b1355b207b66230c7744ca03e7c2b
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+++ b/examples/ssl/vgg16_cifar_ssl_channels_training.yaml
@@ -0,0 +1,92 @@
+# SSL: Channel regularization
+# We compressed the compute from 313M MACs to 85M MACs (x3.7), and the parameters from 14.7M to 725K (x20) using SSL
+# with channel-wise regularization, with a drop of 0.19% Top1 accuracy and w/o muuch effort.
+#
+# time python3 compress_classifier.py --arch vgg16_cifar  ../../../data.cifar10 -p=50 --lr=0.05 --epochs=180 --compress=../ssl/vgg16_cifar_ssl_channels_training.yaml -j=1 --deterministic
+#
+# The results below are from the SSL training session, and you can follow-up with some fine-tuning:
+# time python3 compress_classifier.py --arch vgg16_cifar  ../../../data.cifar10 --resume=checkpoint.vgg16_cifar.pth.tar --lr=0.01 --epochs=20
+# ==> Top1: 91.010    Top5: 99.480    Loss: 0.513
+#
+# Parameters:
+# +----------+---------------------------+------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
+# |          | Name                      | Shape            |   NNZ (dense) |   NNZ (sparse) |   Cols (%) |   Rows (%) |   Ch (%) |   2D (%) |   3D (%) |   Fine (%) |     Std |     Mean |   Abs-Mean |
+# |----------+---------------------------+------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------|
+# |  0.00000 | features.module.0.weight  | (31, 3, 3, 3)    |           837 |            837 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.32376 | -0.00329 |    0.23517 |
+# |  1.00000 | features.module.2.weight  | (47, 31, 3, 3)   |         13113 |          13113 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.07184 | -0.00374 |    0.04210 |
+# |  2.00000 | features.module.5.weight  | (98, 47, 3, 3)   |         41454 |          41454 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.04835 | -0.00384 |    0.03511 |
+# |  3.00000 | features.module.7.weight  | (117, 98, 3, 3)  |        103194 |         103194 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.03096 | -0.00500 |    0.02305 |
+# |  4.00000 | features.module.10.weight | (193, 117, 3, 3) |        203229 |         203229 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.02948 | -0.00345 |    0.02259 |
+# |  5.00000 | features.module.12.weight | (164, 193, 3, 3) |        284868 |         284868 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.01766 | -0.00233 |    0.01313 |
+# |  6.00000 | features.module.14.weight | (24, 164, 3, 3)  |         35424 |          35424 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.01755 | -0.00082 |    0.01235 |
+# |  7.00000 | features.module.17.weight | (15, 24, 3, 3)   |          3240 |           3240 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.04479 |  0.00043 |    0.03239 |
+# |  8.00000 | features.module.19.weight | (9, 15, 3, 3)    |          1215 |           1215 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.08220 |  0.00163 |    0.06065 |
+# |  9.00000 | features.module.21.weight | (7, 9, 3, 3)     |           567 |            567 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.12944 |  0.00961 |    0.09122 |
+# | 10.00000 | features.module.24.weight | (5, 7, 3, 3)     |           315 |            315 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.17881 |  0.02638 |    0.12776 |
+# | 11.00000 | features.module.26.weight | (7, 5, 3, 3)     |           315 |            315 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.18656 |  0.03477 |    0.12432 |
+# | 12.00000 | features.module.28.weight | (512, 7, 3, 3)   |         32256 |          32256 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.02310 |  0.00009 |    0.01329 |
+# | 13.00000 | classifier.weight         | (10, 512)        |          5120 |           5120 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.10157 | -0.00002 |    0.07181 |
+# | 14.00000 | Total sparsity:           | -                |        725147 |         725147 |    0.00000 |    0.00000 |  0.00000 |  0.00000 |  0.00000 |    0.00000 | 0.00000 |  0.00000 |    0.00000 |
+# +----------+---------------------------+------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
+#
+# Total sparsity: 0.00
+# --- test ---------------------
+# 10000 samples (256 per mini-batch)
+# Test: [   10/   39]    Loss 0.454324    Top1 90.664062    Top5 99.609375
+# Test: [   20/   39]    Loss 0.450643    Top1 90.722656    Top5 99.511719
+# Test: [   30/   39]    Loss 0.441285    Top1 90.807292    Top5 99.557292
+# Test: [   40/   39]    Loss 0.458055    Top1 90.740000    Top5 99.580000
+# ==> Top1: 90.740    Top5: 99.580    Loss: 0.458
+
+lr_schedulers:
+  training_lr:
+    class: StepLR
+    step_size: 45
+    gamma: 0.10
+
+regularizers:
+  Channels_groups_regularizer:
+    class: GroupLassoRegularizer
+    reg_regims:
+      features.module.0.weight: [0.0008, Channels]
+      features.module.2.weight: [0.0008, Channels]
+      features.module.5.weight: [0.0008, Channels]
+      features.module.7.weight: [0.0008, Channels]
+      features.module.10.weight: [0.0008, Channels]
+      features.module.12.weight: [0.0008, Channels]
+      features.module.14.weight: [0.0008, Channels]
+      features.module.17.weight: [0.0008, Channels]
+      features.module.19.weight: [0.0008, Channels]
+      features.module.21.weight: [0.0008, Channels]
+      features.module.24.weight: [0.0008, Channels]
+      features.module.26.weight: [0.0008, Channels]
+      features.module.28.weight: [0.0008, Channels]
+
+    threshold_criteria: Mean_Abs
+
+extensions:
+  net_thinner:
+      class: 'ChannelRemover'
+      thinning_func_str: remove_channels
+      arch: 'vgg16_cifar'
+      dataset: 'cifar10'
+
+policies:
+  - lr_scheduler:
+      instance_name: training_lr
+    starting_epoch: 45
+    ending_epoch: 300
+    frequency: 1
+
+# After completeing the regularization, we perform network thinning and exit.
+  - extension:
+      instance_name: net_thinner
+    epochs: [179]
+
+  - regularizer:
+      instance_name: Channels_groups_regularizer
+      args:
+        keep_mask: True
+    starting_epoch: 0
+    ending_epoch: 180
+    frequency: 1