1
$\begingroup$

I was watching a video of policy gradient by Andrej Karpathy at 10:00 there shows an equation for supervised learning for image classification.

$max\sum _{i}log \:p(y_i|x_i)$

I have worked with image classification models before but I always used a minimizing cost function aka loss function. I also never seen some one using maximizing cost function for image classification in the wild.

  • Why image classification tasks are dominated by loss functions?
  • What is the advantages of a minimizing loss function over a maximizing loss function in image classification?
  • Other than RL where else maximizing cost functions are used?
$\endgroup$
1
$\begingroup$

There is really no difference between minimizing a cost function and maximizing a value function. One can be the reciprocal of the other, or the negative of the other, for example.

$\endgroup$
  • $\begingroup$ It is worth noting that the function that the OP gives is in fact the negative of the usual multi-class loss function. I would hazard a guess that Andrej used it to make a more direct comparison with RL. $\endgroup$ – Neil Slater Oct 14 at 6:56

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.