diff --git a/02_Noise2Void/exercise.ipynb b/02_Noise2Void/exercise.ipynb index 990a397..6a92097 100644 --- a/02_Noise2Void/exercise.ipynb +++ b/02_Noise2Void/exercise.ipynb @@ -231,7 +231,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Checkpoint 1: What is N2V really doing?

\n", + "

Checkpoint 1: N2V masking

\n", "
" ] }, @@ -389,16 +389,6 @@ "careamist = CAREamist(source=training_config)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext tensorboard\n", - "%tensorboard --logdir logs/lightning_logs" - ] - }, { "attachments": {}, "cell_type": "markdown", diff --git a/03_COSDD/exercise.ipynb b/03_COSDD/exercise.ipynb index b8ef461..b781074 100644 --- a/03_COSDD/exercise.ipynb +++ b/03_COSDD/exercise.ipynb @@ -340,7 +340,7 @@ "source": [ "\n", "\n", - "COSDD is a Variational Autoencoder~\\cite{chenvariational} (solid arrows) trained to model the distribution of noisy images $\\mathbf{x}$. \n", + "COSDD is a Variational Autoencoder (solid arrows) trained to model the distribution of noisy images $\\mathbf{x}$. \n", "The autoregressive (AR) decoder models the noise component of the images, while the latent variable models only the clean signal component $\\mathbf{s}$.\n", "In a second step (dashed arrows), the \\emph{signal decoder} is trained to map latent variables into image space, producing an estimate of the signal underlying $\\mathbf{x}$.\n", "{\\bf b):}\n", @@ -603,7 +603,7 @@ "outputs": [], "source": [ "lowsnr_path = \"./../data/mito-confocal-lowsnr.tif\"\n", - "n_test_images = 10\n", + "n_test_images = 5\n", "# load the data\n", "test_set = tifffile.imread(lowsnr_path)\n", "test_set = test_set[:n_test_images, np.newaxis]\n", diff --git a/04_DenoiSplit/exercise.ipynb b/04_DenoiSplit/exercise.ipynb index 16ec2ae..85eaeda 100644 --- a/04_DenoiSplit/exercise.ipynb +++ b/04_DenoiSplit/exercise.ipynb @@ -796,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "4d103864", + "id": "fc2ed8e0", "metadata": {}, "source": [ "

End of the exercise

\n",