Learning representations of data is an important issue in machine learning. Though GAN has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead. GAN tends to produce the data simply without any information about the manifold of the data, which hinders from controlling desired features to generate. Moreover, most of GAN’s have a large size of manifold, resulting in poor scalability. In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data to generate and learns a representation of the data efficiently. Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution. As the larger scale of latent space caused by deploying various distributions in one latent space makes training unstable while maintaining the dimension of latent space, we need to separate the process of defining the distributions explicitly and operation of generation. We prove that a VAE is proper for the former and modify a loss function of VAE to map the data into the pre-defined latent space so as to locate the reconstructed data as close to the input data according to its characteristics. Moreover, we add the KL divergence to the loss function of LSC-GAN to include this process. The decoder of VAE, which generates the data with the corresponding features from the pre-defined latent space, is used as the generator of the LSC-GAN. Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method to generate desired data stably and efficiently, achieving a high compression ratio that can hold about 24 pixels of information in each dimension of latent space. Besides, our model learns the reverse of features such as not laughing (rather frowning) only with data of ordinary and smiling facial expression. Developing generative model is a crucial issue in artificial intelligence. Creativity was a human proprietary, but many recent studies have attempted to make machines to mimic it. There has been an extensive research on generating data and one of them, generative adversarial network (GAN), has led to significant achievements, which might be helpful to deep learning model because, in general, lots of data result in good performance BID12 . Many approaches to creating data as better quality as possible have been studied: for example, variational auto-encoder (VAE) BID9 and GAN BID4 . The former constructs an explicit density, resulting in an explicit likelihood which can be maximized, and the latter constructs an implicit density BID3 . Both can generate data from manifold which is hidden to us so that we cannot control the kind of data that we generate.Because it is costly to structure data manually, we need not only data generation but also automatically structuring data. Generative models produce only data from latent variable without any other information so that we cannot control what we want to generate. To cope with this problem, the previous research generated data first and found distributions of features on latent space by investigating the model with data, since the manifold of data is hidden in generative models. This latent space is deceptive for finding an area which represents a specific feature of our interest; it would Figure 1 : Examples of the manifold. Left: a complex manifold which can be seen in general models, Right: a relatively simple manifold in the proposed model. The midpoint M of A and B can be easily calculated in the right manifold, but not in the left one. The midpoint of A and B is computed as N in the left manifold, which is incorrect. take a long time even if we can find that area. Besides, in the most of research, generative models had a large latent space, resulting in a low compression rate which leads to poor scalability. To work out these problems, we propose a model which can generate the data whose type is what we want and learn a representation of data with a higher compression rate, as well. Our model is based on VAE and GAN. We pre-define distributions corresponding to each feature and modify the loss function of VAE so as to generate the data from the latent variable which follows the specific distribution according to its features. However, this method makes the latent space to become a more complex multimodal distribution which contains many distributions, resulting in an instability in training the LSC-GAN. We prove that this problem can be solved and even made more efficiently by using an auto-encoder model with the theorem in Section 3. Although the proposed model compresses the data into small manifold, it is well-defined with Euclidean distance as shown in Fig. 1 , which compares the manifolds in general models and in our model. The distance can be calculated with Euclidean distance in adjacent points but not in far points at the left manifold in Fig. 1 . However, in the right manifold, we can calculate the distance between points regardless of the distance of them, where we can recognize the manifold more easily as shown in the left side. Thanks to a relatively simple manifold, it can produce neutral features regardless of their location in latent space, so that all features can be said as independent to each other. Our main contribution is summarized as follows.• We propose a method to improve the stability of a LSC-GAN with LSC-VAE by performing the weight initialization, and prove it theoretically.• We achieve conditional generation without additional parameters by controlling the latent space itself, rather than adding additional inputs like the existing model for condition generation.• We propose a novel model that automatically learns the ability to process data continuously through latent space control.• Finally, we achieve an efficient compression rate with LSC-GAN based on weight initialization of LSC-VAE.The rest of the paper is organized as follows. Section 2 reviews the related works and the proposed LSC-GAN model is illustrated in Section 3. In Section 4, we evaluate the performance of the proposed method with some generated data. The conclusion and discussion are presented in Section 5. In this paper, we address some of significant issues in generative models: unstable training, hidden manifold of data, and extensive hardware resource. To generate a data whose type is what we want, we propose a novel model LSC-GAN which can control a latent space to generate the data that we want. To deal with a larger scale of latent space cause by deploying various distributions in one latent space, we use the LSC-VAE and theoretically prove that it is a proper method. Also, we confirm that the proposed model can generate data which we want by controlling the latent space. Unlike the existing generative model, the proposed model deals with features continuously, not discretely and compresses the data efficiently.Based on the present findings, we hope to extend LSC-GAN to more various datasets such as ImageNet or voice dataset. In future work, we plan to conduct more experiments with various parameters to confirm the stability of model. We will also experiment by reducing the dimension of the latent space to verify that the proposed model is efficient. Besides, since the encoder can project the data to the latent space according to the features inherent in data, it could be used as a classifier.
We propose a generative model that not only produces data with desired features from the pre-defined latent space but also fully understands the features of the data to create characteristics that are not in the dataset.
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