Image Quality Assessment Techniques Improve Training and Evaluation of Energy-Based Generative Adversarial Networks
We propose a new, multi-component energy function for energy-based Generative Adversarial Networks (GANs) based on methods from the image quality assessment literature. Our approach expands on the Boundary Equilibrium Generative Adversarial Network (BEGAN) by outlining some of the short-comings of the original energy and loss functions. We address these short-comings by incorporating an l1 score, the Gradient Magnitude Similarity score, and a chrominance score into the new energy function. We then provide a set of systematic experiments that explore its hyper-parameters. We show that each of the energy function*s components is able to represent a slightly different set of features, which require their own evaluation criteria to assess whether they have been adequately learned. We show that models using the new energy function are able to produce better image representations than the BEGAN model in predicted ways.
 Quick summary: This paper proposes an energy based formulation to the BEGAN model and modifies it to include an image quality assessment based term. The model is then trained with CelebA under different parameters settings and results are analyzed. Quality and significance: This is quite a technical paper, written in a very compressed form and is a bit hard to follow. Mostly it is hard to estimate what is the contribution of the model and how the results differ from baseline models. Clarity: I would say this is one of the weak points of the paper - the paper is not well motivated and the results are not clearly presented. Originality: Seems original. Pros: * Interesting energy formulation and variation over BEGAN Cons: * Not a clear paper * results are only partially motivated and analyzed
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Image Quality Assessment Techniques Improve Training and Evaluation of Energy-Based Generative Adversarial Networks
We propose a new, multi-component energy function for energy-based Generative Adversarial Networks (GANs) based on methods from the image quality assessment literature. Our approach expands on the Boundary Equilibrium Generative Adversarial Network (BEGAN) by outlining some of the short-comings of the original energy and loss functions. We address these short-comings by incorporating an l1 score, the Gradient Magnitude Similarity score, and a chrominance score into the new energy function. We then provide a set of systematic experiments that explore its hyper-parameters. We show that each of the energy function*s components is able to represent a slightly different set of features, which require their own evaluation criteria to assess whether they have been adequately learned. We show that models using the new energy function are able to produce better image representations than the BEGAN model in predicted ways.
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>



 Quick summary: This paper proposes an energy based formulation to the BEGAN model and modifies it to include an image quality assessment based term. The model is then trained with CelebA under different parameters settings and results are analyzed. Quality and significance: This is quite a technical paper, written in a very compressed form and is a bit hard to follow. Mostly it is hard to estimate what is the contribution of the model and how the results differ from baseline models. Clarity: I would say this is one of the weak points of the paper - the paper is not well motivated and the results are not clearly presented. Originality: Seems original. Pros: * Interesting energy formulation and variation over BEGAN Cons: * Not a clear paper * results are only partially motivated and analyzed
assistant
 Quick summary: This paper proposes an energy based formulation to the BEGAN model and modifies it to include an image quality assessment based term. The model is then trained with CelebA under different parameters settings and results are analyzed. Quality and significance: This is quite a technical paper, written in a very compressed form and is a bit hard to follow. Mostly it is hard to estimate what is the contribution of the model and how the results differ from baseline models. Clarity: I would say this is one of the weak points of the paper - the paper is not well motivated and the results are not clearly presented. Originality: Seems original. Pros: * Interesting energy formulation and variation over BEGAN Cons: * Not a clear paper * results are only partially motivated and analyzed