GAN DISSECTION: VISUALIZING AND UNDERSTANDING GENERATIVE ADVERSARIAL NETWORKS
Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models.
In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models.
The paper proposes a method for visualizing and understanding GANs representation. This seems an important topic as several such methods were performed for networks trained in supervised learning, which relate
to the predicted outcome, but there is lack of methods for interpreting GANs which are learned in an unsupervised manner and it is generally unclear what is the representation learned by GANs. 
The method is finding correlations between the appearance of objects and the activation of units in each layer of the learned network. 
In addition, the paper presents a 'causal' measure, where a causal effect of a unit is measured by removing and adding this unit from/to the network and computing the average effect on object appearance.
The authors demonstrate how the methods are applied by improving the appearance of images, by modifying units which were detected as important for specific objects. 
The authors also provide an interactive interface where users can manually examine and modify their trained GANs in order to add/remove objects and to remove artifacts. 
The method proposed by the authors seem to be appropriate for convolutional neural networks, where 'units' in each layer may correspond to objects and can be searched for in particular locations of image. 
It is not clear to me if and how one can apply the author's methods to other architecture, and to other application domains (besides images), or whether the method is limited to vision applications. 
The authors do not explain specifically how do they choose the 'units' for which they seek interpretation when reporting their results. It is written that each layer is divided into two sets: 
u  and u-bar, where we seek interpretation of u. But how large does u tend to be? how would one choose it? is it one filter out of all filters in a certain layer? when optimizing for sets of units together
(using the alpha probabilities and the optimization in eq. 6) what is d? is it performed for all units in a single layer? more details would be useful here. 
The paper is overall clearly written, with lots of visual examples demonstrating the methods presented in it. 
The paper presents a new methodological idea, which allows for nice practical contribution. There is no theoretical contribution or any deep analysis. 
There is no reference in the paper to the supp. info. figures and therefore it is not clear if and how the supp. info. adds valuable information to the reader. 
The authors use scores like SWD and FIT for performance, but give no explanations for what do these scores measure. 
Minor: 
Abstract: immprovements -> improvements 
Page 6, middle: 'train on four LSUN' -> 'trained on four LSUN'
Page 7, bottom: Fig. 14a and 14b should be Fig. 8a and 8b