Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: ## Summary
This blog post recaps FCDD (Fully Convolutional Data Description), a recent explainable approach to deep anomaly detection. FCDD employs a fully convolutional architecture, which preserves spatial information and thus outputs feature maps instead of vectors. Using an adaptation of the hypersphere classification loss, a semi-supervised version of the unsupervised one-class objective, FCDD trains its output feature map to directly be a downsampled anomaly heatmap. For full-resolution anomaly heatmaps, FCDD provides an upsampling technique based on a transposed convolution with a fixed Gaussian kernel that corresponds to the receptive field size of the convolutional neural network.

## Review
This blog post does a great job of visualization. The figures are very illustrative and clear. I like how the blog inserts experiments from the original paper's appendix to explain various aspects of FCDD. I also like the structure and flow of the blog, which makes it readable and easy to follow. The authors start with a motivating introduction and also come up with some personal intuition about what anomaly detection is, which makes it easy to understand. They quickly, but precisely, cover most of the related work. Though they seem to consider the state of research at the point where the paper was written, which now is a bit outdated. 

One important drawback is that the blog misses explaining Outlier Exposure, a recent technique FCDD uses to incorporate random natural images as training outliers, keeping the overall objective unsupervised. I think at least one sentence would really help here. Otherwise, the reader might wonder why FCDD is unsupervised, as the objective clearly uses labels distinguishing between nominal and anomalous training samples. This is also where confetti noise comes into play (as training outliers, just as the Outlier Exposure samples), which is unclear from the blog. 

Another weak point of the blog is that the blog does a suboptimal job of explaining how FCDD is able to create anomaly heatmaps. All other points (especially the upsampling and training objective) are nicely explained. However, to me, it's not clear from the blog how using fully convolutional networks in combination with the Hypersphere objective makes the pixels in FCDD's output matrix become anomaly scores for the respective input regions. I think extending the "Fully Convolutional architecture" section and spending more time on how receptive fields work might help here. 

Overall, the blog provides a really nice summary of the FCDD paper, which is helpful for a quick overview. However, it could do a better job of explaining the heatmap generation, it barely provides any novel insights about the method, and it lacks a bit of contribution towards making the paper more "accessible." In conclusion, this is borderline to me, with a tendency towards acceptance. 


#### Pros
*	Motivating introduction using both playful (Among Us) and serious examples (medicine, etc.).
*	Illustrative clean figures, useful!
*	Blog post has a nice flow and inserts additional experiments that were part of the original paper's appendix to explain various aspects of the method. 



#### Cons
*	An explanation of what makes FCDD unsupervised (Outlier Exposure, synthetic anomalies) is missing.
*	The explanation of how FCDD is able to generate anomaly heatmaps could be more thorough.
*	The blog post does not provide new insights into FCDD. 



#### Major Remarks
*	Second figure. Autoencoders are unsupervised (or maybe self-supervised) but not supervised. They don't use labels. 


#### Minor Remarks / Typos
*  Fix: "Finding tumors for cancer, cracks inside metal bars, fraud detection are a few of the very common applications." \
	E.g., "[...], metal bars, or frauds are [...]."

*	Fix: "An Anomaly Detector model [...]" \
	Either "anomaly detector" or "anomaly detection model."

*	Fix: "These are usually trained on a nominal (no anomalies present) dataset and used to reconstruct anomalous samples, which it is then expected to reconstruct poorly." \
	E.g., ", which are then expected to be resconstructed poorly."

*	Repetitive word then: "Then a model is trained for predicting the transformation used and then an anomaly score is generated via the confidence score of the prediction."

*	The blog sometimes just says "The paper" or "In this particular paper" when referring to the FCDD paper. This can be confusing. I'd suggest making this clearer and saying something like "In the FCDD paper" or so. 


* Fifth and eighth figure. Caption below. The text says "Generated anomaly heat maps," while in the rest of the blog, the word heatmap is written as one word. 

* Fix: "[...] then the objective function as described in the image below maps nominal samples near the origin centered hypersphere and anomalies to be mapped away." \
	E.g., "[...] maps nominal samples near the origin centered hypersphere and anomalies away."

* Fourth figure. Green text. Use of "maximises" instead of "maximizes" (as used in the rest of the blog). Stick to AE or BE. 

* Fix: "[...] so we cannot learn in a supervised fashion the upsampling mapping" \
	E.g., "so we cannot learn the upsampling in a supervised fashion"

* Ninth figure. Maybe remove the blue and green curve. It confused me a bit at first since it makes the figure "overloaded."

* The semi-supervised equation requires a definition of $m$. Should be $h \cdot w$. It's kind of obvious from the text but should be defined properly. 

* Fix comma: "The FCDD approach where heatmaps as explanations are generated directly as output, has also generated an interesting by-product observation." 


review_title: Decent overview of FCDD (Explainable Deep One-Class Classification) with lack of novel insights

Meta-review: We appreciated the effort made to give a high level idea of the paper but perhaps it would have needed a slightly deeper insight and more precise explanations about technical but aspects of the work. We would also have appreciated the authors to take a step back and give us new insights into this literature / problem. 
Decision: Reject