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MSE vs Cross Entropy for training with facial landmark (pose) heatmaps

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0

$begingroup$

I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.

Ground Truth and Loss

For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.

I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!

loss = tf.losses.mean_squared_error(
 predictions=heatmaps, labels=labels_tensor
)

When I tried with cross entropy, I am getting better results. But they are not sharper

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')

gtmap means groundtruth map.

tensorflow cnn loss-function heatmap

share|improve this question

asked 36 mins ago

azmathazmath

101

New contributor

azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

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add a comment | 

0

$begingroup$

I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.

Ground Truth and Loss

For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.

I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!

loss = tf.losses.mean_squared_error(
 predictions=heatmaps, labels=labels_tensor
)

When I tried with cross entropy, I am getting better results. But they are not sharper

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')

gtmap means groundtruth map.

tensorflow cnn loss-function heatmap

share|improve this question

asked 36 mins ago

azmathazmath

101

New contributor

azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

$begingroup$

I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.

Ground Truth and Loss

For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.

I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!

loss = tf.losses.mean_squared_error(
 predictions=heatmaps, labels=labels_tensor
)

When I tried with cross entropy, I am getting better results. But they are not sharper

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')

gtmap means groundtruth map.

tensorflow cnn loss-function heatmap

share|improve this question

asked 36 mins ago

azmathazmath

101

New contributor

azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

loss = tf.losses.mean_squared_error(
 predictions=heatmaps, labels=labels_tensor
)

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')

share|improve this question

asked 36 mins ago

azmathazmath

101

New contributor

azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked 36 mins ago

azmathazmath

101

New contributor

azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 36 mins ago

azmathazmath

101

New contributor

azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked 36 mins ago

azmathazmath

101