Summary the following text in bullet points: "NAME_1 \etal~\cite{NAME_2combining} developed a method, which utilizes a compositional model in combination with CNN features to predict the object class in occluded images. This approach has later been improved to be differentiable and, thus, end-to-end trainable \cite{NAME_2compositional}. Compositional models are generative models and have to be trained using multiple losses in conjunction with maximum-likelihood estimation. In contrast to their method, our approach is purely discriminative, much simpler to implement, directly combinable with standard CNNs, and does not require any additional loss terms other than a classification loss for training.

Moreover, several data augmentation techniques were proposed in the last years \cite{devries2017cutout,yun2019cutmix}, which introduced artificial occlusion in the training process. However, NAME_1 \etal~\cite{NAME_2compositional} show that these augmentations alone only add limited robustness against partial occlusion to the network. We argue that this happens due to the network focusing more on the occluded areas and how to interpret them instead of focusing on the visible object parts and utilizing them to generate a conclusive classification decision. 

NAME_3 \etal~\cite{NAME_4tdapnet} presented a convolutional network architecture with an attention mechanism, which masks out occluded features in the network to improve its robustness against occlusion. However, NAME_1 \e