Creating a good probabilistic model for images is a challenging task, due to the large variability in natural images. For general photographs, an ideal generative model would have to cope with scene layout, occlusion, variability in object appearance, variability in object position and 3D rotation and illumination effects like shading and shadows. The formidable challenges in creating such a model have led many researchers to pursue discriminative models, which instead use image features that are largely invariant to many of these sources of variability. In this talk, I will compare both approaches and describe some strengths and weaknesses of each and suggest some directions in which the best aspects of both can be combined.