Transfer Learning in Autonomous Vehicles using Convolutional Networks

University essay from Lunds universitet/Matematik LTH

Abstract: This thesis has investigated the potential benefits of using transfer learning when training convolu- tional neural networks for the task of autonomously driving a car. Three transfer learning networks were trained and compared with a conventionally trained convolutional neural network. The first conclusion is that training, as expected, is considerably quicker when using transfer learning. More specifically, transfer learning utilizes pretrained networks and does not require the entire network to be trained, rather just parts of it. This results in faster training since less weights have to be updated per epoch of training. Here, training was approximately twice as fast per epoch compared to training a non-transfer learning network. The pretrained network used in this thesis was in fact also trained, something that in a practical transfer learning setting would already have been done. The data used to train the so called pretrained network originates from a different, although similar, domain. By keeping a varying number of convolutional layers from the pretrained network fixed, three networks were trained using transfer learning. This way, knowledge is said to be transferred from a previously solved problem to the current problem. The network called fine-tuning network 2 performed better than the two other transfer learning networks when tested in the simulator. After only 7 epochs of training, this network could drive around the entire track without going off-road, even at higher speeds. This is to be compared with the so called conventional network, trained under a classic machine learning setting. Not until having been trained for 18 epochs did the performance of the conventional network match the performance of the fine-tuning network 2, trained for merely 7 epochs. In other words, in less than half the number of epochs, and with each epoch training twice as fast, a network using transfer learning performed as well as a non-transfer learning network.

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