Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises.
In this work, we show that highly-structured semantic hierarchy emerges from the generative representations as the variation factors for synthesizing scenes.
By probing the layer-wise representations with a broad set of visual concepts at different abstraction levels, we are able to quantify the causality between the activations and the semantics occurring in the output image.
Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes.
The qualitative and quantitative results suggest that the generative representations learned by GAN are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme.
Identifying such a set of manipulatable latent semantics facilitates semantic scene manipulation.
Success of deep neural networks stems from the representation learning, which identifies the explanatory factors underlying the high-dimensional observed data (Bengio et al. (2013) ).
Prior work has shown that many concept detectors spontaneously emerge inside the deep representations trained for classification task.
For example, Gonzalez-Garcia et al. (2018) shows that networks for object recognition are able to detect semantic object parts, and Bau et al. (2017) confirms that deep representations from classifying images learn to detect different categorical concepts at different layers.
Analyzing the deep representations and their emergent structures gives insight into the generalization ability of deep features (Morcos et al. (2018) ) as well as the feature transferability across different tasks (Yosinski et al. (2014) ).
But current efforts on interpreting deep representations mainly focus on discriminative models (Zhou et al. (2015) ; Gonzalez-Garcia et al. (2018) ; Zeiler and Fergus (2014) ; Agrawal et al. (2014) ; Bau et al. (2017) ).
Recent advance of Generative Adversarial Networks (GANs) (Goodfellow et al. (2014) ; Karras et al. (2018a; ; Brock et al. (2019) ) is capable of transforming random noises into high-quality images, however, the nature of the learned generative representations and how a photo-realistic image is being composed over different layers of the generator in GAN remain much less explored.
It is known that the internal units of Convolutional Neural Networks (CNNs) emerge as object detectors when trained to categorize scenes (Zhou et al. (2015) ).
Representing and detecting informative categorical objects provides an ideal solution for classifying scenes, such as sofa and TV are representative of living room while bed and lamp are of bedroom.
However, synthesizing a scene demands far more knowledge for the generative models to learn.
Specifically, in order to produce highly-diverse scene images, the deep representations might be required to not only generate every individual object relevant to a specific scene category, but also decide the underlying room layout as well as render various scene attributes, e.g., the lighting condition and color scheme.
Very recent work on interpreting GANs Bau et al. (2019) visualized that the internal filters at intermediate layers are specialized for generating some certain objects, but studying scene synthesis from object aspect only is far from fully understanding how GAN is able to compose a photo-realistic image, which contains multiple variation factors from layout level, category level, to attribute level.
The original StyleGAN work (Karras et al. (2018b) ) pointed out that the layer-wise latent codes actually control the synthesis from coarse to fine, but how these variation factors are composed together and how to quantify such semantic information are still uncertain.
Differently, this work gives a much deeper interpretation on the hierarchical generative representations in the sense that we match these layer-wise variation factors with human-understandable scene variations at multiple abstraction levels, including layout, category (object), attribute, and color scheme.
Starting with the state-of-the-art StyleGAN models (Karras et al. (2018b) ) as the example, we reveal that highly-structured semantic hierarchy emerges from the deep generative representations with layer-wise stochasticity trained for synthesizing scenes, even without any external supervision.
Layerwise representations are first probed with a broad set of visual concepts at different abstraction levels.
By quantifying the causality between the layer-wise activations and the semantics occurring in the output image, we are able to identify the most relevant variation factors across different layers of a GAN model with layer-wise latent codes: the early layers specify the spatial layout, the middle layers compose the category-guided objects, and the later layers render the attributes and color scheme of the entire scene.
We further show that identifying such a set of manipulatable latent variation factors from layouts, objects, to scene attributes and color schemes facilitates the semantic image manipulation with large diversity.
The proposed manipulation technique is further generalized to other GANs such as BigGAN (Brock et al. (2019) ) and ProgressiveGAN (Karras et al. (2018a) ).
Disentanglement of Semantics.
Some variation factors we detect in the generative representation are more disentangled with each other than other semantics.
Compared to the perceptual path length and linear separability described in Karras et al. (2018b) and the cosine similarity proposed in Shen et al. (2019) , our work offers a new metric for disentanglement analysis.
In particular, we move the latent code along one semantic direction and then check how the semantic scores of other factors change accordingly.
As shown in Fig.8(a) , when we modify the spatial layout, all scene attributes are barely affected, suggesting that GAN learns to disentangle layout-level semantic from attribute-level.
However, there are also some scene attributes (from same abstraction level) entangling with each other.
Taking Fig.8(c) as an example, when modulating "indoor lighting", "natural lighting" also varies.
This is also aligned with human perception, further demonstrating the effectiveness of our proposed quantification metric.
Application to Other GANs.
We further apply our method for two other GAN structures, i.e., PGGAN (Karras et al. (2018a) ) and BigGAN (Brock et al. (2019) ).
These two models are trained on LSUN dataset (Yu et al. (2015) ) and Places dataset ) respectively.
Compared to StyleGAN, PGGAN feeds the latent vector only to the very first convolutional layer and hence does not support layer-wise analysis.
But the proposed re-scoring method can still be applied to help identify manipulatable semantics, as shown in Fig.9(a
) . BigGAN
is the state-of-the-art conditional GAN model that concatenates the latent vector with a class-guided embedding code before feeding it to the generator, and it also allows layer-wise analysis like StyleGAN. Fig.9(b
) gives
analysis results on BigGAN from attribute level, where we can tell that scene attribute can be best modified at upper layers compared to lower layers or all layers. Meanwhile
, the quantitative curve shows consistent result with the discovery on StyleGAN as in Fig.3(a) . These
results
demonstrate the generalization ability of our approach as well as the emergence of manipulatable factors in other GANs.
In this paper, we show the emergence of highly-structured variation factors inside the deep generative representations learned by GANs with layer-wise stochasticity.
In particular, the GAN model spontaneously learns to set up layout at early layers, generate categorical objects at middle layers, and render scene attribute and color scheme at later layers when trained to synthesize scenes.
A re-scoring method is proposed to quantitatively identify the manipulatable semantic concepts within a well-trained model, enabling photo-realistic scene manipulation.
We show that highly-structured semantic hierarchy emerges in the deep generative representations as a result for synthesizing scenes.