SlowGuess/NAACL_2021_OCR huggingface dataset:
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A Architecture Details of Information Maximization
A.1 The structure of discriminator in text-label mutual information maximization module
The discriminator  $D_{MI}$  consists of two 1D-convolutional layers with kernels of size 3 and three linear layers. The architecture of  $D_{MI}$  is shown in Figure 2 and the details of all the layers are shown in Table 4 ("-" indicates that there is no activation for the corresponding layer). As shown in Figure 2, the discriminator  $D_{MI}$  takes pairs of text representation and label representation as input. The text representations are fed to the convolutional layers first, then the label representations are concatenated with the output from the convolutional layers and fed to the following linear layers. The final linear layer produces a score for each pair of text sample and corresponding labels.
Layers	Size (Input)	Size (Output)	Activation
1D-conv layer	300	300	ReLU
1D-conv layer	300	512	-
Linear layer	812	512	ReLU
Linear layer	512	512	ReLU
Linear layer	512	1	-
Table 4: Layer details of the discriminator  ${D}_{MI}$  .
Figure 2: The structure of discriminator  $D_{MI}$ .
A.2 The structure of discriminator in label prior matching
The discriminator  $D_{pr}$  in the label prior matching module is composed of three linear layers. The details of these layers are shown in Table 5. This discriminator takes label representation as input and is applied for each label to compute its prior matching loss as stated in Section 2.1.2.
Layers	Size (Input)	Size (Output)	Activation
Linear layer	300	1000	ReLU
Linear layer	1000	200	ReLU
Linear layer	200	1	Sigmoid
Table 5: Layer details of the discriminator  ${D}_{pr}$  .