From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Accept

From: NIPS.cc/2018/Workshop/IRASL/Paper37/AnonReviewer1
title: Accept
review: An interesting approach, and, if I understand the results correctly, very promising results.


rating: 5: Top 15% of accepted papers, strong accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper37/AnonReviewer2
title: Domain Adaptation employing task specific loss enforced Cyclic Adversarial Learning 
review: In this paper, authors proposed to use Cyclic Adversarial Learning by enforcing task specific loss for domain adaptation in audio and visual domain tasks. 

Strength:
This paper is well-written and well organized to a large extent as well as well referenced.
With proper experimental setup (specifically, for speech recognition) the quality of this paper could be improved further.

Weakness:
The main weakness of this paper is in experimental design, specifically speech domain adaptation task. 
Here, details about speech recognition framework are missing. 
Authors could have chosen domain mismatch problem due environmental mismatch between source and target domains.
This is much more real-world scenario than gender mismatch problem. 
I think, standard speech recognition paradigm usually tackle this problem (gender/speaker mismatch) by incorporating speaker adaptive training (SAT). 
I am wondering whether authors have prepared any baseline with SAT to compare with their proposed approach.
In this paper, authors reported results (PER) on TIMIT corpus. So, it is difficult to say whether proposed approach will generalized to other corpora.  
 

rating: 3: Marginally above acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/IRASL/Paper37/AnonReviewer3
title: Robust Domain Adaptation By Augmented Cyclic Adversarial Learning
review: The paper extends the standard Cycle GAN by replacing the reconstruction objective with a task-specific model. The L1 reconstruction loss (which operates pixel by pixel) is indeed “too restrictive” and the introduction of a task-specific model encourages a more “semantic” mapping. This way we basically switch from an unsupervised to a semi-supervised setting. The proposed extension, that is similar to what proposed in the “Limitations and Discussion” section of the original Cycle GAN paper, seems particularly effective when little adaptation data are available. 

Pros:
The paper is very clear and well-written.
The idea is well described and well motivated.
Cons:
TIMIT is a very limited task. It is good as a showcase, but I would recommend the authors to address a more realistic scenario. For instance, this approach can be used to environmental adaptation or for the so-called speech enhancement.

To improve replicability of the results, it would be better to add an appendix showing all the details of the considered architecture.  For instance, which kind of neural architecture has been used for the ASR (convolutional, fully-connected, recurrent)? Which type of ASR has been used (hybrid HMM-DNN, CTC,...)? Have you used a decoder to retrieve the final sequence of phonemes? Are you using a phoneme language model? It could be important to add all of these details.

rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct