From 1b59ccea4c161fbf0ddfae2c3d10c1ef36d66f39 Mon Sep 17 00:00:00 2001 From: Alex Ausch Date: Fri, 4 Aug 2023 15:07:43 -0400 Subject: [PATCH 1/2] detetion -> detection --- lab2/Part2_FaceDetection.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lab2/Part2_FaceDetection.ipynb b/lab2/Part2_FaceDetection.ipynb index c877149c..64fc42ed 100644 --- a/lab2/Part2_FaceDetection.ipynb +++ b/lab2/Part2_FaceDetection.ipynb @@ -515,7 +515,7 @@ "source": [ "## 2.5 Semi-supervised variational autoencoder (SS-VAE)\n", "\n", - "Now, we will use the general idea behind the VAE architecture to build a model to automatically uncover (potentially) unknown biases present within the training data, while simultaneously learning the facial detection task. This draws direct inspiration from [a recent paper](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf) proposing this as a general approach for automatic bias detetion and mitigation.\n" + "Now, we will use the general idea behind the VAE architecture to build a model to automatically uncover (potentially) unknown biases present within the training data, while simultaneously learning the facial detection task. This draws direct inspiration from [a recent paper](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf) proposing this as a general approach for automatic bias detection and mitigation.\n" ] }, { From 8dc09a112edc1b8cf136e1e8b1e9714142fcc0a8 Mon Sep 17 00:00:00 2001 From: Alex Ausch Date: Fri, 4 Aug 2023 15:23:37 -0400 Subject: [PATCH 2/2] occurances - occurences --- lab2/Part2_FaceDetection.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lab2/Part2_FaceDetection.ipynb b/lab2/Part2_FaceDetection.ipynb index 64fc42ed..fc4c2333 100644 --- a/lab2/Part2_FaceDetection.ipynb +++ b/lab2/Part2_FaceDetection.ipynb @@ -638,7 +638,7 @@ " # Build the decoder network using the Sequential API\n", " decoder = tf.keras.Sequential([\n", " # Transform to pre-convolutional generation\n", - " Dense(units=4*4*6*n_filters), # 4x4 feature maps (with 6N occurances)\n", + " Dense(units=4*4*6*n_filters), # 4x4 feature maps (with 6N occurences)\n", " Reshape(target_shape=(4, 4, 6*n_filters)),\n", "\n", " # Upscaling convolutions (inverse of encoder)\n",