In an additional embodiment, if there are multiple auto-encoders in order to achieve variable bitrate, neural decoder network overfitting may allow using a lower- dimensional auto-encoder so that the PSNR is similar to the one obtained by a higher-dimensional auto-encoder but with a lower bitrate, thus resulting into higher compression efficiency. In the example of various embodiments, there is one entity preforming the encoding or compression of a data, here referred to as the “transmitter”, and one entity performing the decoding or de-compression of the encoded data, here referred to as the “receiver”. Thus, the transmitter may comprise the neural encoder network, and the receiver may comprise the neural decoder network. Although terms “transmitter” and “receiver” are used, it should be noticed that the solution is not limited only to applications where encoded data is transmitted from the transmitter to the receiver. For example, the encoded data may be saved on the same device from which is will be decoded, or it may be saved on another device which will be used for moving the data to another memory device from which the decoder will decoded the encoded data. In various embodiments, the auto-encoder has been trained on a large collection of images, such as ImageNet, Places 2, or similar datasets. This pre-trained auto- encoder is referred in this disclosure to as “baselineauto-encoder”or “baseline network”or simply as “baseline”. The encoder and decoder of the autoencoder, will be referred to as the “baselineencoder”and “baselinedecoder”respectively, or simply as “baselines”, when it is clear that both encoder and decoder are being referred to. The training of the baseline network is assumed to have been performed for the task of image (or other specific data) compression, thus by using at least one reconstruction loss and at least one compression loss. The baseline network will be the starting point for overfitting either the encoder or the decoder or both. Here, the baseline network is considered to be a neural network which is able to compress and decompress well any piece of data which is not too different from the training data. Thus, it is a neural network which is able to generalize. Although having such generalization characteristics is beneficial, the present embodiments are not restricted to this requirement. As mentioned, neural network can be optimized (i.e. “overfitted” or “fine-tuned”) on a specific input data. Such optimization operation is a training operation, thus