and Questions 1. For the distribution estimation, this paper uses three Gradient flow networks to learn different parameters. According to Section 4.3, the networks learn specific parameters for each class using different inputs (i.e., x ¯ j
). That is to say, the network F 1
will output c j j = 1 n for n
classes. However, in Line 142, the authors point that the parameter c
is shared between all classes. How to unify this c
? 2. How to update F 2
, and F 3
via minimizing Eq. (16)? When the classifiers are fixed, it seems that only the network F 1
can be trained. 3. Some experimental details are missing. 3.1. What is the ratio of training data D t
to validation data D v
in the training stage. 3.2. What is the value of initial c
. 4. In Table 2, the metric-based baseline FEAT performs similar accuracy to the proposed method. It’s better to discuss the superiority of the method in terms of time consumption. According to Algorithm 2, the upper bound Eq. (14) simplifies the training of classifiers, but Eq. (16) is still difficult to compute. Typo: In Table 2, the description does not match the content, e.g., "Euclidean Metric" (or "Hyperbolic Metric") and "Model".
2. How to update F 2 , and F 3 via minimizing Eq. (16)? When the classifiers are fixed, it seems that only the network F 1 can be trained.