Limited by the challenge of insufficient training data, research into lifelog analysis, especially visual lifelogging, has not progressed as fast as expected. To advance research on object detection on visual lifelogs, this thesis builds a deep learning model to enhance visual lifelogs by utilizing other sources of visual (non-lifelog) data which is more readily available. By theoretical analysis and empirical validation, the first step of the thesis identifies the close connection and relation between lifelog images and non-lifelog images. Following that, the second phase employs a domain-adversarial convolutional neural network to trans- fer knowledge from the domain of visual non-lifelog data to the domain of visual lifelogs. In the end, the third section of this work considers the task of visual object detection of lifelog, which could be easily extended to other related lifelog tasks. One intended outcome of the study, on a theoretical level of lifelog research, is to iden- tify the relationship between visual non-lifelog data and visual lifelog data from the perspective of computer vision. On a practical point of view, a second intended outcome of the research is to demonstrate how to apply domain adaptation to enhance learning on visual lifelogs by transferring knowledge from visual non-lifelogs. Specifically, the thesis utilizes variants of convolutional neural networks. Furthermore, a third intended outcome contributes to the release of the corresponding visual non-lifelog dataset which corresponds to an existing visual lifelog one. Finally, another output from this research is the suggestion that visual object detection from lifelogs could be seamlessly used in other tasks on visual lifelogging.