Recent progress in image recognition has stimulated the deployment of vision systems (e.g. image search engines) at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Meanwhile, existing image processing methods only optimize for better human perception, whereas the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we propose simple approaches to improve machine interpretability of processed images: optimizing the recognition loss directly on the image processing neural network or through an intermediate transforming model, a process which we show can also be done in an unsupervised manner. Interestingly, the processing model's ability to enhance the recognition performance can transfer when evaluated on different recognition models, even if they are of different architectures, trained on different object categories or even different recognition tasks. This makes the solutions applicable even when we do not have the knowledge about future downstream recognition models, e.g., if we are to upload the processed images to the Internet. We conduct comprehensive experiments on three image processing tasks with two downstream recognition tasks, and confirm our method brings substantial accuracy improvement on both the same recognition model and when transferring to a different one, with minimal or no loss in the image processing quality.
The goal in this work is to improve machine interpretability of images.
The authors main claims are:
-	Their proposed approach improves image recognition accuracy even without knowing subsequent recognition tasks and recognition models used to perform them (transferable model to different recognition models/tasks).
-	For this they propose what they call “Recognition-Aware” processing that combines image processing loss and recognition loss.
-	The approach is evaluated on three image processing tasks with two downstream recognition tasks:
o	Image super-resolution, de-noising, and JPEG-de-blocking processing tasks, with 
o	Image classification and object detection recognition tasks.
The paper is well written and organized, experiments carried are extensive but the reuse of known neural networks, many simplifications (shortcuts), a not clear enough methodology (see below), limited processing & recognition tasks used to support it, do not justify in our opinion the main (over-arching) work’s claim:
-	In 3.2 optimizing recognition loss/Last paragraph:  “Interestingly, we find that image processing models trained with the loss of one recognition model R1, can also boost the performance when evaluated using recognition model R2, even if model R2 has a different architecture, recognizes a different set of categories or even is trained for a different task.”.
The paper would greatly benefit (to understand the context of the work or the explanations provided) from clarification of the many under-defined, not clearly introduced concepts it carries:
-	Meaning of “Network” is not clearly defined:
o	Abstract: “image processing network”.
o	Introduction: “the network maps an image to a semantic label”
o	Later in the paper only networks introduced are deep neural networks. That should be clear from beginning of the paper.
-	“Retraining/Adaptation”  in 1st paragraph page 2.
-	In 1. Introduction/Paragraph 1: You use “.. techniques .. have been proposed for making the output images look natural to human”:
o	Noise is part of nature. A de-noised (smoothed) image is not “more natural”.
o	Enhanced (processed) images are not necessarily “more” natural, rather they take advantage of the human visual perception characteristics to enhance recognition for example.
-	In 1. Introduction/Paragraph 3:
o	“.. of great importance that the processed images be recognizable”  Should explain the concept of image recognition! Because it could be related to contained objects, overall description (for captioning for example) etc. 
-	“Image processing” in the context of the paper is intended only as “image enhancement for recognition”. Pattern detection, segmentation, object extraction etc. are not included in this restrictive definition. Should specify for example: image enhancement and restoration. 
-	Figure 1: As an illustration, it’s completely counterproductive for your discourse as many simple image recognition algorithms would recognize the bird even in the noisy image.
-	In 3. Unsupervised optimization of recognition loss: The “unsupervised RA” process is not  clear enough to us especially the statement:
o	“.. only “unsupervised” for training model P, but the target pre-trained model R can still be trained in full supervision.”.
-	“We may not know what network architectures (e.g. ResNet or VGG) will be used for inference, what object categories the downstream model recognizes (e.g. animals or scenes), or even what task will be performed on the processed image (e.g. classification or detection)”. 
o	Is your goal a universal “recognition model” applicable to anything?
-	I also have some trouble with the terminology:  
o	In 1. Introduction/Paragraph 4: “It is also important that the enhanced machine semantics is not specific to any concrete recognition model”: “enhanced machine semantics”!
o	In 1. Introduction/Paragraph 4: “..transferable among different recognition architectures..”. Does “architectures” refer to deep neural networks (DNN)? If yes, is recognition performed only by DNN? What about the preceding bullet (“is not specific to any concrete recognition model”)?
-	In 1. Introduction/Paragraph 3: 
o	 “.. we argue that image processing systems should maintain/enhance machine semantics”. Do not see what’s to argue here?
o	“Recognition-Aware Image Processing” is it simply put Image Processing techniques for recognition enhancement (“Recognition” still needs to be defined)?
-	In 2 Related work :
o	 “ .. we assume we do not have the control on the recognition model, as it might be on the cloud or decided in the future, thus we advocate adapting the image processing model only. This also ensures the recognition model is not harmed on natural images.” Care to explain?
o	: “to achieve better recover the face identity from low-resolution images”, Typo?
-	In 1. Introduction/Paragraph 1:
o	“ .. might not look “natural” to machines”: Care to explain this concept? 
	Would advise to just keep the second part of the sentence.
-	In 1. Introduction/Paragraph 2: “One could specifically train a recognition model only on these output images produced by the de-noising model to achieve better performance on such images, but the performance on natural images can be harmed.”  Care to explain?. 
o	More complicated images (noisier, multiple obstructions etc.) are recognized nowadays and true to actual applications.
-	3.4 using an intermediate transformer/Last paragraph:
o	“ .. that there are two instances for each image (the output of model P and T), one is “for human” and the other is “for machines”.”:
	The “Transformer” characteristics are not clearly defined for the intended output (For machines?).
	Why is output of model T not represented in Figure 2 (Right)?