@conference {7067,
	title = {Geometry-Free View Synthesis: Transformers and no 3D Priors},
	booktitle = {Proceedings of the Intl. Conf. on Computer Vision (ICCV)},
	year = {2021},
	url = {https://arxiv.org/abs/2104.07652},
	author = {Robin Rombach and Patrick Esser and Bj{\"o}rn Ommer}
}
@conference {7054,
	title = {High-Resolution Complex Scene Synthesis with Transformers},
	booktitle = {CVPR 2021, AI for Content Creation Workshop},
	year = {2021},
	abstract = {The use of coarse-grained layouts for controllable synthesis of complex scene images via deep generative models has recently gained popularity. However, results of current approaches still fall short of their promise of high-resolution synthesis. We hypothesize that this is mostly due to the highly engineered nature of these approaches which often rely on auxiliary losses and intermediate steps such as mask generators. In this note, we present an orthogonal approach to this task, where the generative model is based on pure likelihood training without additional objectives. To do so, we first optimize a powerful compression model with adversarial training which learns to reconstruct its inputs via a discrete latent bottleneck and thereby effectively strips the latent representation of high-frequency details such as texture. Subsequently, we train an autoregressive transformer model to learn the distribution of the discrete image representations conditioned on a tokenized version of the layouts. Our experiments show that the resulting system is able to synthesize high-quality images consistent with the given layouts. In particular, we improve the state-of-the-art FID score on COCO-Stuff and on Visual Genome by up to 19\% and 53\% and demonstrate the synthesis of images up to 512 x 512 px on COCO and Open Images.},
	author = {Manuel Jahn and Robin Rombach and Bj{\"o}rn Ommer}
}
@proceedings {7070,
	title = {ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis},
	year = {2021},
	url = {https://arxiv.org/abs/2108.08827},
	author = {Patrick Esser and Robin Rombach and Andreas Blattmann and Bj{\"o}rn Ommer}
}
@proceedings {7053,
	title = {Stochastic Image-to-Video Synthesis usin cINNs},
	year = {2021},
	author = {Michael Dorkenwald and Timo Milbich and Andreas Blattmann and Robin Rombach and Konstantinos G. Derpanis and Bj{\"o}rn Ommer}
}
@proceedings {7028,
	title = {Taming Transformers for High-Resolution Image Synthesis},
	year = {2021},
	url = {https://arxiv.org/abs/2012.09841},
	author = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer}
}
@proceedings {6932,
	title = {A Disentangling Invertible Interpretation Network for Explaining Latent Representations},
	year = {2020},
	abstract = {Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation.},
	url = {https://compvis.github.io/iin/},
	author = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer}
}
@proceedings {6997,
	title = {Making Sense of CNNs: Interpreting Deep Representations \& Their Invariances with INNs},
	year = {2020},
	abstract = {To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly, the invariances they capture turn neural networks into black box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant to. We present an approach based on INNs that (i) recovers the task-specific, learned invariances by disentangling the remaining factor of variation in the data and that (ii) invertibly transforms these recovered invariances combined with the model representation into an equally expressive one with accessible semantic concepts. As a consequence, neural network representations become understandable by providing the means to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black box models by enabling post-hoc interpretations of state-of-the-art networks without compromising their performance.},
	url = {https://compvis.github.io/invariances/},
	author = {Robin Rombach and Patrick Esser and Bj{\"o}rn Ommer}
}
@conference {7012,
	title = {Network Fusion for Content Creation with Conditional INNs},
	booktitle = {CVPRW 2020 (AI for Content Creation)},
	year = {2020},
	month = {2020},
	abstract = {Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications.},
	url = {https://compvis.github.io/network-fusion/},
	author = {Robin Rombach and Patrick Esser and Bj{\"o}rn Ommer}
}
@proceedings {7011,
	title = {Network-to-Network Translation with Conditional Invertible Neural Networks},
	year = {2020},
	abstract = {Combining stimuli from diverse modalities into a coherent perception is a striking feat of intelligence of evolved brains. This work seeks its analogy in deep learning models and aims to establish relations between existing networks by faithfully combining the representations of these different domains. Therefore, we seek a model that can relate between different existing representations by learning a conditionally invertible mapping between them. The network demonstrates this capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into an easily accessible domain. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. In particular, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own.},
	url = {https://compvis.github.io/net2net/},
	author = {Robin Rombach and Patrick Esser and Bj{\"o}rn Ommer}
}
@conference {7025,
	title = {A Note on Data Biases in Generative Models},
	booktitle = {NeurIPS 2020 Workshop on Machine Learning for Creativity and Design},
	year = {2020},
	abstract = {It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes.},
	url = {https://arxiv.org/abs/2012.02516},
	author = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer}
}
