Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper presents PLAD, an anomaly detection methodology that relies on perturbations of the original data with the goal of learning to separate the perturbed data from the original, which helps in the detection of anomalies. An evaluation across multiple datasets and against multiple baseline methodologies provide evidence on the promise of PLAD.
strengths_and_weaknesses: Strengths:

S1. Perturbation-base learning for AD is a timely and important topic

S2. Experimentation with many baselines and open datasets

S3. Code is provided

Weaknesses:

W1. Lack of technical novelty and depth

W2. Experiments used "as is" from other papers

questions: The idea of constructing perturbations and then learning models to separate them from the original data is interesting with good potential for the AD problem. Unfortunately, there are several concerns with this work:

W1. Lack of technical novelty and depth

There is no proper introduction of relevant concepts in preliminaries. The approach reads as if it's the first work using perturbations and goes ahead and presents how the problem is solved with Equations 2/3.  No motivation, nor how these loss functions were obtained is provided. 

In addition, there is a lack of technical depth. What's used in the loss function is more or less existing knowledge. Likely combined together for the first time, but unclear how this advances the area of perturbation-based learning (i.e., the paper reads as an application of such solutions to AD - heavy re-writing is needed in that direction to introduce concepts and prior work that led to PLAD).

W2. Experiments used "as is" from other papers

The experimentation is weak. Even though many baselines are used, results are taken "as is" from previous papers, without detailed evaluation and tuning as the presented approach. 

It's not clear if the results are statistically significantly different. A rigorous statistical analysis is desirable.


limitations: N/A
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes PLAD, i.e., Perturbation Learning-based Anomaly Detection, that learns to inject noise into the normal samples to produce abnormal samples for anomaly detection. The approach has been evaluated on image data as well as tabular data, achieving promising anomaly detection (AD) results.
strengths_and_weaknesses: ## Strengths

1. The presentation is clear. The paper offers a clear presentation with a good explanation of the methodology.
2. Comprehensive comparisons and discussions with related works (e.g., Sec. 3).
3. Good empirical evaluations. PLAD is evaluated on both tabular and image datasets and achieves competitive results.

## Weaknesses
1. The structure of the paper could be more compact. From my perspective, the illustrations of the learned patterns of the injected perturbation are particularly insightful and interesting, and it would be better if they (or some of them, if considering the space limit) can appear in the main text.
questions: It would be interesting to see how PLAD will perform on sequential data (e.g. multivariate time series anomaly detection), as also recognized by the authors. Including one or two experiments in the aspect will greatly improve the generality and application potentials of the proposed method. Even adding some discussions will help a lot.
limitations: The authors have properly discussed the limitations of the work in the conclusion.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors propose an approach to semi-supervised anomaly detection. The core idea is to generate anomalous data using an adaptive perturbator, which modifies the normal data and hence, solve the problem of anomaly detection as a supervised binary classification task.  
strengths_and_weaknesses: The idea is original and the results are promising. It would have been helpful to have the presented approach compared to self-supervised approaches to anomaly detection as these seem well related to the perturbation idea. 
The perturbation to the original data has been chosen to be either additive or multiplicative, it is not discussed what are the limitations on the inferred boundaries of the normal data distribution given only these 2 perturbations. 
questions: What impact does the perturbation operation have on the inferred normal data distribution boundaries? 
How is the approach related and how does it compare to self-supervision based anomaly detection methods?
limitations: It is not clear how the learned class of the perturbed samples can capture samples which can't be obtained by a small perturbation of the normal data class. 
The approach hasn't been tested on time-series anomaly detection as also stated by the authors.
review_title: null

Meta-review: metareview: This paper proposes an anomaly detection approach that learns perturbations directly from the normal training data. Although the reviewers are concerned about the novelty and depth of the approach, they appreciate the comprehensive experimental results. Therefore, I recommend acceptance for the paper.
confidence: Less certain
Decision: Accept