***We thank the reviewer for engagement and offering us the opportunity for further clarification.***
**Parameter ablation:** We agree that our model has a few hyperparameters, but let’s expand on these parameters: code binary constraint, mask binary constraint, code contrastive, code length, code mapping. We examine the effect of each hyperparameter on overall performance of the model on CIFAR10 and CUB datasets. Due to time constraints, we report the results for 20 epochs on CUB and 100 epochs on CIFAR10. Our default values for the hyperparameters are: code constraint coeff$=10$, mask constraint coeff$=10$, code contrastive coeff $\beta=1$, code mapping coeff $\gamma=1$ and code length coeff $\delta=0.1$.
**Code binary constraint:** This hyperparameter is introduced to satisfy the binary requirement of the code. By using binary neural networks and an STE (straight-through estimator) [i], we can simply omit the requirement for this hyperparameter. Another approach would be to benefit from Boltzmann machines [ii] to have a binary code. The following table reports the results for different hyperparameters for this binary code condition. Note that since we use tanh to create the binary vector, the coefficient only determines how fast the method makes the conditions met, and when 0 and 1 are stabilized, the hyperparameter effect will be diminished. However, we noticed that bigger coefficients affect the known accuracy to some degree.
|**Dataset**||CUB||
|----------------------|----------|----------|----------|
|Code constraint coeff|All |Known|Novel|
|1|67.9|82.4|60.7|
|10|68.0|82.3|60.9|
|100|68.3|81.4|61.8|
|1000|67.3|80.2|60.9|
**Mask binary constraint:** For the mask constraint hyperparameter, again we can use a binary equivalent like binary neural network or Boltzmann machine. In our Lagrange multiplier approach, we start from an all-one mask, hence a bigger constraint translates to a bigger code length for categories. Since smaller or longer than required category lengths are both detrimental to the performance, a middle coefficient is more useful.
|**Dataset**||CUB||
|----------------------|----------|----------|----------|
|Mask constraint coeff|All|Known|Novel|
|1|69.9|81.9|63.9|
|10|68.0|82.3|60.9|
|100|71.3|80.3|66.8|
|1000|68.6|81.6|62.1|
**Code contrastive:** This loss is the loss that maintains information about the input. From the following table we observe that for a fine-grained dataset like CUB, minimizing the information will lead to a better performance, however for a generic dataset like CIFAR10, maintaining some information about the input itself can be beneficial especially for novel categories.
|**Dataset**||CUB|||CIFAR|10|
|----------------------|----------|----------|----------|----------|----------|----------|
|$\beta$|All|Known|Novel|All |Known |Novel|
|0.01|70.2|83.6|63.5|94.1|96.8|92.7|
|0.1|66.4|79.8|59.7|94.7|96.8|93.6|
|1|68.0|82.3|60.9|95.8|96.8|95.2|
|2|67.7|80.5|61.3|95.4|96.8|94.8|
**Code length:** Here we report our results for different code length hyperparameters, note that since the code length is penalized exponentially, the code length effect is not comparable to the exponential growth. However from this table it can be seen that the shorter code lengths (higher code length penalty), translates to a better novel category identification. Also note that since CIFAR 10 is a generic dataset, some extra information can actually help the model to distinguish categories from each other, while for the CUB dataset, it is better to omit the extra information.
|**Dataset**||CUB|||CIFAR|10|
|----------------------|----------|----------|----------|----------|----------|----------|
|$\delta$|All| Known|Novel|All|Known|Novel|
|0.01|66.5|79.2|60.2|94.8|96.7|93.8|
|0.1|68.0|82.3|60.9|95.8|96.8|95.2|
|1|70.1|82.1|64.1|94.8|96.8|93.9|
|2|69.1|80.6|63.4|95.3|96.8|94.6|
**Code mapping:** Since current evaluation metrics rely on hit-and-miss accuracy, our category codes must be mapped to this scenario. This loss is not an essential part of the self-coding that our model learns, but it accelerates the model’s training.
|**Dataset**||CUB|||CIFAR|10|
|----------------------|----------|----------|----------|----------|----------|----------|
|$\gamma$|All|Known|Novel|All|Known| Novel|
|0.01|69.3|82.1|62.9|95.0|96.8|94.1|
|0.1|68.3|80.2 |62.4|94.9|96.6 |94.0|
|1|68.0| 82.3|60.9|95.8|96.8|95.2|
|2|68.6| 80.3 |62.7|95.4|96.8|94.6|
One reason the model is not very sensitive to different hyperparameters is that our model consists of three separate parts: Code masker, Code Generator and Categorizer. The only hyperparameters that affect all of these three parts directly are $\beta$, the code contrastive coefficient, and $\delta$, the code length coefficient. This is why these hyperparameters have a much more pronounced effect on our model’s performance. We will include the ablation and discussion on the effect of the hyperparameters on our model in the updated manuscript.