Paper:
Change-point detection problem consists of discovering abrupt property changes in the generation process of time-series. Most state-of-the-art models are optimizing the power of a kernel two-sample test, with only a few assumptions on the distribution of the data. Unfortunately, because they presume the samples are distributed i.i.d., they are not able to use information about the seasonality of a time-series. In this paper, we present a novel approach -ATR-CSPD allowing the detection of changes in the seasonal pattern of a time-series. Our method uses an autoencoder together with a temporal regularization, to learn the pattern of each seasonal cycle. Using low dimensional representation of the seasonal patterns, it is possible to accurately and efficiently estimate the existence of a change point using a clustering algorithm. Through experiments on artificial and real-world data sets, we demonstrate the usefulness of the proposed method for several applications.

Review:
Summary: The paper raises an alarm that state-of-the art change-point detection methods in the ML literature do not handle important practical aspects arising in time-series modeling, namely seasonality. Indeed, methods designed to detect changing distribution under an i.i.d. setting can fail dramatically when the assumption is violated, when the change happens in the seasonal component.  The paper proposes to use an auto-encoder to find the "main pattern" within each seasonal window, and to use total variation penalty (l1-norm on the change) of the hidden state in the auto-encoder to encourage a smooth state-sequence which allow breaks.  They use k-means clustering to partition data-points, and detect a change-point if two consequent hidden states don't end up in the same cluster. 
While the proposal is sensible and the paper is reasonably readable, I find the paper lacking in several respects, and recommend to reject it. My main concerns are 
(a) novelty: despite the claims in the paper -- the importance of seasonality is well known and appreciated in time-series literature, and the proposal to look for changes in seasonality is fairly obvious when dealing with practical time-series. I would suggest to do a comprehensive literature search and re-evaluate the novelty of the paper. 
I believe that recent ML papers e.g. kernel two-sample tests and such, focus on the i.i.d. setting and ignore seasonality (and other messy aspects of practical TS) -- as it is the more challenging statistical problem.
(b) The paper considers a setting where the time-series consists of a seasonal component and an i.i.d. component (combined additively or multiplicatively). It doesn't attempt to model any kind of stochastic dynamics -- e.g. at least a simple auto-regressive model instead of iid, and non-stationarity (trends) in the time-series. So despite aiming to look at practical time-series, the paper still considers a simplified model.
(c) The paper's presentation is often sloppy in language use, assumptions, mathematical details, and simulations and needs to be significantly improved to be considered for ICLR (or related ML conferences). 
Detailed comments: 
a) The references are severely lacking. There is an extensive literature in modeling time-series with seasonality and classical methods such as SARIMA (seasonal ARIMA), or exponential smoothing can track the evolution and changes in seasonal components. Various nonlinear DL-approaches to TS with seasonality have also started to appear. Once time-series is decomposed into trend, seasonal and stochastic part (using any linear or nonlinear or deep model), it is straightforward to apply anomaly detection algorithms to each component separately. Please take a look at e.g. https://anomaly.io/blog/index.html (from salesforce.com), to see practical change-point or anomaly detection in time-series in practice which does pay attention to seasonality. Also papers by Rob Hyndman pay close attention to seasonality, see e.g. https://otexts.com/fpp2/. 
"Changepoint Detection in Periodic and Autocorrelated Time Series", https://journals.ametsoc.org/doi/full/10.1175/JCLI4291.1
https://cran.r-project.org/web/packages/trend/vignettes/trend.pdf  (which has a section on seasonal change-point detection)
Harvey, Koopman, Penzer, "Messy Time Series: A Unified approach", Adv. in Econometrics, Vol. 13, pp. 103-143., https://www.stat.berkeley.edu/~brill/Stat248/messyts.pdf
Perhaps there's relatively less focus on these practical details of change-point detection in recent ML literature and the focus is on the stochastic component, as it is the most challenging for prediction. The use of l1-norm of differences in time-series to detect changes is a natural idea, and has been suggested many papers e.g.in: http://eeweb.poly.edu/iselesni/lecture_notes/TV_filtering.pdf, 
"Time Series Clustering using the Total Variation Distance", 
Stephen Boyd's trend filtering, https://web.stanford.edu/~boyd/papers/pdf/l1_trend_filter.pdf .
While I am not aware of a specific prior work on auto-encoder with temporal smoothness for CPD, most of the main ideas are well known, and in my view the contribution is very limited in novelty.
b) You're ignoring any memory or dynamics in the stochastic component of the time-series -- e.g. allowing something like a simple AR model rather than iid would be a good step. Detecting changes in the dynamics or correlation structure (temporal or cross-sectional) would make the paper more interesting.  Something closer to switching linear dynamical systems, see for example https://arxiv.org/abs/1610.08466. 
c) The presentation has many issues in language / math / simulations and needs to be improved:
1. The setting is not described clearly / formally -- are you trying to detect change-points online or offline, what assumptions are you making on the segments after removing seasonality -- are these just iid / stationary, can they include trends, outliers, e.t.c.
2. Baseline methods for detecting seasonal patterns are naive -- clearly applying methods that are not aware of seasonality will fail when there is strong seasonal components. There is one basic attempt at removing the seasonal component by averaging, and applying iid kernel CPD methods -- where it does help.  I believe doing something a bit more realistic (like doing a seasonal decomposition) will make the baselines much stronger. 
3. Citation format is inconsistent with ICLR.  
4. ATR-CSPD is undefined in the abstract. 
5. Intro:  i, j, k notation inconsistent -- you seem to use i both for i = j*p + k, and also to refer to window id. 
6. What is a "generative function" of time-series? Do you mean the pdf / cdf? What do you mean by a product of generative functions (which is additive or multiplicative), do you mean adding / taking products of random variables coming from independent distributions? What do you mean that you do not differentiate between additive / multiplicative? Do you claim to handle both within the same model?
7. Definition 2 -- do you look for x_jo,k ~ Gk',  or x_j for j> j0 ~ Gk'? 
8. You claim a multi-variate extension is easy -- but is it? How would you tackle e.g. changes in correlation structure?
9. "Autoencoders attempt to copy input to output" - isn't this trivial by using an identity function? You should mention some compression / bottleneck as well. 
10. How do you optimize the total-variation (l1-norm) penalty in your formulation? Just throw it into SGD in keras?
11.  The discussion in 3.2. is confusing -- you talk about weekly series, but use daily-seasonality, however you then describe detecting weekdays vs. weekends? How can you associate separate weekends without a weakly seasonal model? 
12. London electricity data-set -- why do you average all weeks within the time-series to find average customer week? This was very surprising. Don't you loose most of the interesting anomaly data this way?
13. Figures are not explained well. While there's nice use of color -- it's often hard to understand what is the description pointing at. 
typos: Person -> Pearson,  Autencoder -> Autoencoder, and many others.