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Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size (img_col, img_row, n_class), and the final segmentation will be a argmax operation over the last dimension.

Now I want to add a new class to the network, for example, pedestrians, so that after training, the network will be able to detect pedestrians in images.

But I no longer have the old training data. Instead, I've got a new dataset which also contains pedestrians. Training with the new dataset (using the old weights as initialization) will be the most straightforward way, but I would like to hear some other approaches.

Could anyone share some thoughts on how to realize this?

Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size (img_col, img_row, n_class), and the final segmentation will be a argmax operation over the last dimension.

Now I want to add a new class to the network, for example, pedestrians, so that after training, the network will be able to detect pedestrians in images.

But I no longer have the old training data. Instead, I've got a new dataset which also contains pedestrians, but only the pedestrians are labeled. Training with the new dataset (using the old weights as initialization) will be the most straightforward way, but I would like to hear some other approaches.

Could anyone share some thoughts on how to realize this?

Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size (img_col, img_row, n_class), and the final segmentation will be a argmax operation over the last dimension.

Now I want to add a new class to the network, for example, pedestrians, so that after training, the network will be able to detect pedestrians in images.

But I no longer have the old training data. Instead, I've got a new dataset which also contains pedestrians. Training with the new dataset will be the most straightforward way, but I would like to hear some other approaches.

Could anyone share some thoughts on how to realize this?

Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size (img_col, img_row, n_class), and the final segmentation will be a argmax operation over the last dimension.

Now I want to add a new class to the network, for example, pedestrians, so that after training, the network will be able to detect pedestrians in images.

But I no longer have the old training data. Instead, I've got a new dataset which also contains pedestrians. Training with the new dataset (using the old weights as initialization) will be the most straightforward way, but I would like to hear some other approaches.

Could anyone share some thoughts on how to realize this?

Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size (img_col, img_row, n_class), and the final segmentation will be a argmax operation over the last dimension.

Now I want to add a new class to the network, for example, pedestrians, so that after training, the network will be able to detect pedestrians in images.

But I no longer have the old training data. Instead, I've got a new dataset which also contains pedestrians. Training with the new dataset will be the most straightforward way, but I would like to hear some other approaches.

Could anyone share some thoughts on how to realize this?

Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size (img_col, img_row, n_class), and the final segmentation will be a argmax operation over the last dimension.

Now I want to add a new class to the network, for example, pedestrians, so that after training, the network will be able to detect pedestrians in images.

But I no longer have the old training data. Instead, I've got a new dataset which also contains pedestrians. Training with the new dataset will be the most straightforward way, but I would like to hear some other approaches.

Could anyone share some thoughts on how to realize this?