Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper proposes TransBoost to deal with deep transductive learning. The method follows the large margin principle and includes the similarity and confidence of two instances in the loss function. The effectiveness of this method is examined by using a variety of pre-trained neural networks, datasets, and baseline models. It shows good performance compared to inductive classification as well as versus SSL and t-FSL methods
strengths_and_weaknesses: Strengths:
It provides extensive experiments on multiple neural network architectures and datasets and shows good improvement.

Weakness:
The paper is not in the submission format, there is no line number. 
Clarity: some paragraphs are hard to read.
Significance: the author doesn't provide a strong motivation for why their work is important
questions: 1. Some paragraphs are hard to read. For example, in the 3rd paragraph in Section 4, what does it mean: we incentivize test sample pairs that are likely to be different in their classes to also be different in their empirical class probabilities while preserving the prior knowledge of $f_{\theta}$?
2. The toy example in Section 4.2 is unclear:
(a) How is the p(x|f) calculated with SVM?
(b) Although TransBoost loss is lower in T-SVM, does the decision boundary of T-SVM provides better separation of the test instances? So what is the distribution of test instances?
3 Typo?
(a) Section 5.5: separating the different -> separating the difference
(b) Figure 2: ... example indicating ... -> ... example indicates ...
limitations: N/A
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper investigates deep transductive learning where both a labeled training sample and an unlabeled test sample are provided at training time. The goal of deep transductive learning is to achieve high accuracy on the test set. To this end, the authors propose TransBoost as a procedure to fine-tune any deep neural model using a labeled training sample and an unlabeled test sample. Extensive experiments are conducted to verify the effectiveness of the proposed method.

strengths_and_weaknesses: **Pros:**
1. The paper is well-written and well-organized.
2. Investigating conventional statistical machine learning in the context of deep learning is interesting and meaningful.
3. Extensive experiments are conducted to verify the effectiveness of the proposed task and method.

**Cons:**
1. The task of deep transductive learning has a severe issue of overfitting target test set. Although Transboost does not use the true label of the test set, it implicitly uses pseudo labels, i.e. similarity of image pairs. In this sense, the performance would boost a lot in any deep models as shown in Fig.1.

questions: Please see details in Cons.  I would like to suggest the authors define a meaningful task. Eqn.(2) borrows ideas from TSVM and it would have a positive effect in test time training [1]

[1] Sun et al. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts. ICML 2020.

limitations: Yes
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: Paper proposes TransBoost, a transductive learning loss function that improves performance in transductive image classification in domains where large datasets and test sets are available. The work draws inspiration from TSVM and proposed a transductive loss regularization term that discriminates unlabelled examples that have similar class probabilities but shouldn't. Extensive experiments demonstrate that TransBoost improves performance on various architectures, datasets, and settings. Ablation studies provide interesting insights into the behaviour of TransBoost under different conditions.
strengths_and_weaknesses: Strengths:
- Paper is incredibly well-written and very easy to follow.
- TransBoost is an interesting and relatively novel idea. It effectively bridges the gap in performance from t-FSL methods that have been widely developed. The algorithmic choices are sound and well-motivated.
- Experiments are extensive and provide sufficient evidence to establish state-of-the-art performance.

(Small )Weaknesses:
- Certain results suggest that TransBoost is more applicable to some architectures than others. Although performance improvements are universal, some methods improve much more, and some even surpass their otherwise more inductively powerful counterparts. This suggests that TransBoost is algorithmically better suited for certain architectures, but the reasoning is primarily speculated. It would be useful to see further analysis as to why for instance, Transformer based architectures benefit from TransBoost?
- One would presume that alternatively modifying the loss function to group together similar samples would benefit performance, albeit not to the same extent. However, the observation that it degrades performance may suggest that transductive learning can produce fragile optimization manifolds that should be carefully designed. Further elaboration on this front could be useful.
questions: Suggestions are included in the above sections. An overall strong submission that could be further strengthened by additional analysis of TransBoosts uneven behaviour with respect to architectures.
limitations: Yes, the authors provide an in-depth analysis of the technical limitations of the work and provide directions for future research. There is, however, no explicit discussion of societal impacts. I believe the work could benefit from a concise analysis of its societal impact and if any could potentially be negative.
review_title: null

Meta-review: metareview: This paper initially received mixed opinions. After intensive author-reviewer and reviewer-reviewer discussions, all reviewers converged and recommended acceptance. AC recommends accepting the paper.
confidence: Certain
Decision: Accept