Thanks for your valuable reply, KFrank.
Actually, If this is a common two-class classification, such as DOG/CAT classification, there is no doubt for me to up-sample the DOG pictures to make the numbers for DOG and CAT same. Come back to the DOG/Not DOG task, intuitively, I up-sampled the DOG pictures 9 times and on the contrary，the DOG class was optimized faster than Not DOG class. It’s confusing and interesting! And then, I su-sampled the DOG class 4 or 5 times, it seemed that DOG/Not DOG were optimized Synchronously.
I try to explain such experimental result. In the task of DOG/CAT classification, intra-class samples have similar distribution and they push the dicision boundery to another class in almost the same direction. However, there is a little different in the task of DOG/Not DOG task I think. In such a task, CAT and other ANIMALS are treating as negtive class together, but they still have different data distribution. They push the decision boundery to pos class. Some of them are resultant force，but not all of them. So when up-sample the DOG pictures 9 times, the optimization of DOG class is more dominant, because the samples in DOG class are all resultant force.
I’d like to hear what you think of the problem. Thanks again for your reply. Hope to continue the disscusion with you!