Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
We propose a new algorithm for training generative adversarial networks to jointly learn latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). In practice, this means that by fixing the identity portion of latent codes, we can generate diverse images of the same subject, and by fixing the observation portion we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose. Our algorithm features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce images that are both photorealistic, distinct, and appear to depict the same person. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to accommodate pairwise training. Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm’s ability to generate convincing, identity-matched photographs.
 Quality The paper is well written and the model is simple and clearly explained. The idea for disentangling identity from other factors of variation using identity-matched image pairs is quite simple, but the experimental results on faces and shoes are impressive. Clarity The model and its training objective are simple and clearly explained. Originality There are now many, many papers on generative models with disentangled feature representations, including with GANs. However, to my knowledge this is the first paper showing very compelling results using this particular setup of identity-aligned images. Significance Disentangled generative models are an important line of work in my opinion. This paper presents a very simple but apparently effective way of disentangling identity from other factors, and implements in two of the more recent GAN architectures. Suggestion for an experiment - can you do few shot image generation? A simple way to do it would be to train an encoder from image → identity encoding. Then, given one or a few images of a new person’s face or a new shoe, you could estimate the identity latent variable, and then generate many additional samples. Pros - Very simple and effective disentangling technique for GANs. - Great execution, compelling samples on both faces and shoes. Cons - Only two factors of variations are disentangled in this model. Could it be generalized to specify more than just two, e.g. lighting, pose, viewpoint, etc? - Not much technically new or surprising compared to past work on disentangling generative models.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
We propose a new algorithm for training generative adversarial networks to jointly learn latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). In practice, this means that by fixing the identity portion of latent codes, we can generate diverse images of the same subject, and by fixing the observation portion we can traverse the manifold of subjects while maintaining contingent aspects such as lighting and pose. Our algorithm features a pairwise training scheme in which each sample from the generator consists of two images with a common identity code. Corresponding samples from the real dataset consist of two distinct photographs of the same subject. In order to fool the discriminator, the generator must produce images that are both photorealistic, distinct, and appear to depict the same person. We augment both the DCGAN and BEGAN approaches with Siamese discriminators to accommodate pairwise training. Experiments with human judges and an off-the-shelf face verification system demonstrate our algorithm’s ability to generate convincing, identity-matched photographs.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Quality The paper is well written and the model is simple and clearly explained. The idea for disentangling identity from other factors of variation using identity-matched image pairs is quite simple, but the experimental results on faces and shoes are impressive. Clarity The model and its training objective are simple and clearly explained. Originality There are now many, many papers on generative models with disentangled feature representations, including with GANs. However, to my knowledge this is the first paper showing very compelling results using this particular setup of identity-aligned images. Significance Disentangled generative models are an important line of work in my opinion. This paper presents a very simple but apparently effective way of disentangling identity from other factors, and implements in two of the more recent GAN architectures. Suggestion for an experiment - can you do few shot image generation? A simple way to do it would be to train an encoder from image → identity encoding. Then, given one or a few images of a new person’s face or a new shoe, you could estimate the identity latent variable, and then generate many additional samples. Pros - Very simple and effective disentangling technique for GANs. - Great execution, compelling samples on both faces and shoes. Cons - Only two factors of variations are disentangled in this model. Could it be generalized to specify more than just two, e.g. lighting, pose, viewpoint, etc? - Not much technically new or surprising compared to past work on disentangling generative models.
assistant
 Quality The paper is well written and the model is simple and clearly explained. The idea for disentangling identity from other factors of variation using identity-matched image pairs is quite simple, but the experimental results on faces and shoes are impressive. Clarity The model and its training objective are simple and clearly explained. Originality There are now many, many papers on generative models with disentangled feature representations, including with GANs. However, to my knowledge this is the first paper showing very compelling results using this particular setup of identity-aligned images. Significance Disentangled generative models are an important line of work in my opinion. This paper presents a very simple but apparently effective way of disentangling identity from other factors, and implements in two of the more recent GAN architectures. Suggestion for an experiment - can you do few shot image generation? A simple way to do it would be to train an encoder from image → identity encoding. Then, given one or a few images of a new person’s face or a new shoe, you could estimate the identity latent variable, and then generate many additional samples. Pros - Very simple and effective disentangling technique for GANs. - Great execution, compelling samples on both faces and shoes. Cons - Only two factors of variations are disentangled in this model. Could it be generalized to specify more than just two, e.g. lighting, pose, viewpoint, etc? - Not much technically new or surprising compared to past work on disentangling generative models.