Are there any way of removing impact of a certain data from a trained model (about "right to forget") I was reading about wearable technologies (Recent Advances in Wearable Sensing Technologies). They briefly talk about Right to forget and a question came to my mind. Suppose that we trained a deep learning model (e.g., CNN) by using face images collected from 1000 participants (10 face images from each participant = 10x1000 images in sum). After training, participant 1 wanted to remove his/her face data from the model. Instead of re-train the model with 999 participants, are there any way of removing the impact of participant 1's data from the trained model? I made a quick search but I could not find it. Did you hear about something like that?
PS: When I think about the forward and backward propagation processes and how we decrease the error, it seems to me to be not possible.

It is possible, but amounts to the same effort as retraining the model.

The weights $\theta$ at iteration $t$ (a mini-batch within an epoch) are defined as:
$$\theta_t=\theta_{t-1}-\nabla_{\theta_{t-1}}\mathcal L_{t-1}$$
By recursion, it becomes obvious that:
$$\theta_t=\theta_0-\sum_{i=0}^{t-1}\nabla_{\theta_i}\mathcal L_i$$
Where $\mathcal L_i$ is the mini-batch loss function at a given iteration (again, mini-batch within epochs).
So, even when said data-point you want to remove is not included in the mini-batch, its effect is still present in the current state of the weights due to the previous iterations, where it was used to derive a gradient.
To fully remove the effects of a data-point, you'd have to backtrack weights all the way back to the first time it was used to derive gradients.
Then, you'd calculate it's individual contribution to the gradient and remove that.
But, the next iteration would be using a new set of weights, which means that the new loss function calculation would need to be redone.
In other words, it's the same effort of retraining the whole model in most applications (I'm sure some very specific training schemes and architectures might allow for simpler solutions).
