SCAD: Super-Class-Aware Debiasing for Long-Tailed Semi-Supervised Learning
Authors: Sunguk Jang
Jinwoo Jeon
Byung-Jun Lee
Venue: ICLR 2026 Poster (2026)
Keywords: long-tailed
semi-supervised learning
intra-super-class imbalance
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: In long-tailed semi-supervised learning (LTSSL), pseudo-labeling often creates a vicious cycle of bias amplification. Recent methods attempt to mitigate this issue via logit adjustment (LA). However, LA-based debiasing remains inherently hierarchy-agnostic and fails to account for semantic relationships between classes. We reveal a critical yet overlooked problem of \textit{intra-super-class imbalance}, where semantically similar classes within a super-class are both highly confusable and locally imbalanced. This combination reinforces early mistakes, causing minority-class representations to be suppressed by their majority neighbors. To break this cycle, we propose Super-Class-Aware Debiasing (SCAD), a framework that performs dynamic, super-class-aware logit adjustment. SCAD leverages latent semantic structure to concentrate its corrective power on the most confusable groups, thereby resolving local imbalances. Extensive experiments demonstrate that SCAD achieves state-of-the-art performance.

Published as a conference paper at ICLR 2026
SCAD:SUPER-CLASS-AAAAE DEBIASING FOR LONG-
TAILED SEMI-SUPERVISED LEARNING
Sunguk Jang" AITRICS
Jinwoo Jeon' Korea University
Byungjun Lee Korea University
ABSTRACT
In long-tailed semi-supervised learning (LTSSL), pseudo-labeling often creates a vicious cycle of bias amplification. Recent methods attempt to mitigate this issue via logit adjustment (LA). However, LA-based debiasing remains inher- ently hicrarchy-agnostic and fails to account for semantic relationships between classes. We reveal critical yet overlooked problem of intra-super-clas imbal- ance, where semantically similar classes within a super-class are both highly con- fusable and locally imbalanced. This combination reinforces early mistakes, caus- ing minority-class representations to be suppressed by their majority neighbors. To break this eycle, we propose Super-Class-Are Aware Debiasing (SCAD), frame- work that performs dynamic, super-class-ave aware logit adjustment. SCAD leverages latent semantic structure to concentrate its corrective power on the most confus- able groups, therehy resolving local imbalances. Extensive experiments demon- strate that SCAD achieves state-of-the-an performance. The code is available https::/gittii com aitrrcsttiiiiieeie ttm. SCAD
INTRODUCTION
Deep learning models have achieved remarkable success on large, annotated datasets, but creating them is expensive (Deng et al., 2009 Lin et al., 2014). Semi-supervised learning (SSL. is a pow- erful approach reduce this cost by leveraging small labeled dataset alongside a large unlabeled dataset (Sohn et al. 2020 Berthelot et al. 2020). However, the performance of SSL is often hin- dered by the long-tailed distribution of woal-world data (Figure [ap. where a few majority classes vastly outnumber many minority classes (Kang et al., 2020 Cui et al. 2019). This imbalance is particularly problematic because it creates vicious cycle long-tailed semi-supervised learning (LTSSL). This feedback loop then reinforces bias through biased pseudo-labels and causes severe performance drops for minority classes (Kim et al. 2020 Wei et al. 2021
To break this vicious cycle, logit adjustment (LA) (Menon et al. 2021 powerful method from su- pervised long-tail learning, has become the de facto standard for pseudo-label debiasing in LTSSL. LA applies static, corrective offset to each class's logits, computed from the global class frequen- cies. The appeal of this approach lies in its theoretical guarantee. LA provides a Fisher-consistent correction for any given class prior with negligible overhead, making it an ideal foundational block for more complex scenarios. Building upon this foundation, recent state-of-the. made remarkable progress by deriving more sophisticated estimates of the true prior to leverage within the LA framework, such as by estimating the distribution mismatch (Wei & Gan 2023) or measuring the classifir''s 's intrinsic bias (Lee & Kim 2024). However, while these methods innovate in how they estimate the global prior, the adjustment scheme itself remains the same as LA. It uses a single, uniform correction that is inherently blind to semantic relationships between classes.
methods have
In this paper, we uncover that this blindness to semantic relationships among classes leads to crit- ical challenge we term intra super-sse-- imbalance. This problem arises from the combination of two factors, best illustrated by truck and automobile on CIFAR 10-LT. First, for classes with high se- mantic similarity, LA-based adjustment offers no remedy because its offset is derived solely from class frequencies and is agnostic to inter-class similarities that drive confusion. Second, the issue is amplified by extreme local imbalance within the shared vehicle super-class, where automobile
Equal contribution.

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Published as a conference paper at ICLR 2026
4000
14.00
12.00
11
740
10:0
siplec maondble
(a) Global Distribution
(b) Local Distribution
(c) FixMatch + LA
(d) FixMatch + SCAD
Figure 1: Illustration of the intra-super-class imbalance problem on CIFARIO-LT. (a) The over-
all dataset exhibits long-tailed distribution. (b) The problem arises from a combination of high semantic similarity and extreme local imbalance, as seen within the vehicle super-class. (c) This highlights a limitation of LA (Menon et al.] (02T), which struggles to distinguish minority classes from their majority neighbors. (d) In contrast, SCAD mitigates these critical misclassification.
vastly outnumbers truck (Figure lb As result, LA's globally derived correction is fundamen- tally mismatched to this local conflict. boost for truck is set by global rarity rather than the intense competition it faces from automobile, and thus fails to counteract the dominant class. In pseudo-labeling, this dual failure triggers a self-reinforcing loop of bias amplification, leading to representation overshadowing and the systematic misclassification shown in Figure Ic
To tackle this problem, we propose Super- Class-Awarrr ware Debiasing (SCAD), a framework that per- forms dynamic, super-class-awe aware logit adjustment. Our key insight is that debiasing should adapt to each sample's s predicted super- clase, tailoring the correction to the local set of semantically con- fusable classes. SCAD operationalizes this idea by (i) discovering a latent super-class structure from class names using pre-trained text encoders (e.g., CLIP (Radford et al. 202T)), (ii) training a super- class classifier infer the semantic context for each unlabeled sample, and (iii) refining standard LA with a super-class-aware correction that focuses the adjustment on classes that compete within the predicted super-class, weighted by the super-class posterior. As result, SCAD mitigates sys- tematic misclassifications on CIFAR1O-LT (Figure [d] and alleviates analogous intra-super-class confusions on more complex long-tailes benchmarks (Figure Appendix (A3)).
To validate our approach, we conduct extensive experiments across LTSSL benchmarks, ranging from CIFAR-10/100-15 to large-scale and fine-grained datasets such as ImageNet-127 and Food- 101-LT. We show that SCAD is plug-and-play framework that consistently improves several state- of-the-art methods under distribution- beled data). For example, when combined with ACR on CIFAR-100-LT under severe distribution mismatch, SCAD improves accuracy by up to 3.4%. On ImageNet- 127, SCAD surpasses prior methods by up to 5%, showing that SCAD mitigates label-scarcity snddey-cerity indueed representation chal- lenges, including intra- murra-supe--llas confusions.
distributiin-mismaaeh mismatch settings (e.g., prior shift between labeled and unla-
RELATED WORK
Long-tailed Semi-supervised Learning LTSSL addresses the realistic scenario where labeled data is both scarce and imbalanced. Early methods focused on directly manipulating pseudo-labels, for instance, by iterative re-balancing (Wei et al.) 2021) or refining their distributions (Kim et al.) 2020 Wang al. 2022). Subsequently, logit adjustment (LA) (Menon al. (021) emerged as the de facto standard for debiasing, offering a simple correction by directly adjusting the model's output logits. The effectiveness and simplicity of LA have made the foundational block for recent methods that address distribution mismatch between labeled and unlabeled data. In this setting, the pseudo-label prior can deviate substantially from the labeled prior. These methods, such as ACR (Wei & Gan 2023) and CDMAD (Lee & Kim, 2024), dynamically modulate the strength of the LA correction. ACR achieves this by estimating the mismatch between labeled and unlabeled distributions, while CDMAD does by measuring the classifier's ' intrinsic bias. However, their adjustment schemes remain fundamentally that of LA. Consequently, they are inherently hierarchy- agnostic and sub-optimal for alleviating the localized confusion between semantic neighbors.
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Published as a conference paper at ICLR 2026
Soltmax
log+
Sy
Super-alass Classifier
p(liu))))
f+
Truck
Correct!
Unlabaled Image Sample
Automooilee
Extractor
Super Class Debisaing
Automobile
Wrong!
Class Classifeer
Logit Adjustment
log x
Figure 2: Illustration of Super-Class-Awar -lare Debiasing (SCAD) mechanism. For unlabeled truck image, the class classifier initially produces biased logits, incorrectly favoring automobile. Standard LA applies uniform, global correction, which is insufficient to change the outcome. In contrast, our mechanism uses a super-class classifier to infer the sample's context (vehicle). this context, pkk(uy) is used to compute targeted local adjustment. When this local adjustment is added to the standard LA, it helps overcome the initial bias and boosts the logit for the correct class.
Hierarchical and Super-class learning Leveraging class hierarchies to improve recognition is well established. In supervised learning, methods like HD-CNN Yan et al., 2015) and Deep- RTC (Wu et al. 2020) use manually defined hierarchies, while SuperDisco (Du et al. 2023) dis- covers super-classed from image features via graph neural network. However, these approaches are less practical for LTSSL, as they often require predefined taxonomies or computationally inten- sive training. Additionally, the resulting hierarchies be sensitive the representation used to construct them, which may be unreliable in the low-label regime. In contrast, SCAD is designed be lightweight and practical: constructs super-classes by applying a pre-trained text encoder to class names, without manual hierarchies or additional vision pretraining. This makes SCAD easy to integrate into existing LTSSL pipelines with minimal engineering effort. It then serves simple, Mug-and-plas debiasing module for existing LTSSL methods.
METHOD
We propose Super-Class-Aware Debiasing (SCAD), multi-stage framework to address supe-cuper- imbalance in LTSSL. SCAD consists of three components: (i) super-class dis- covery from semantic information, (ii) dedicated training procedure for the feature extractor and two classifiers, and (iii) a dynamic logit adjustment mechanism that corrects biased predictions at inference time. We detail each component below, after introducing notation and problem setup.
3.1 PRELIMINARIES
3.1.1 PROBLEM SETUP
labeled dataset and D [ an unlabeled dataset, where 2y, Uy Rd denote the d-dimensional inputs. We consider C classes, indexed by E [C] é .1,..., C). Let N, the number of labeled samples in class cadd M, the (generally unknown) number of unlabeled samples in class The labeled set is long-tailed with N1 N2 2 > No and imbalance ratio N/No. We similarly define the unlabeled imbalance ratio Tu = max, Mc/ min, Mo.
Let D' (((r,,
Our model consists of three main components: feature extractor fo Rd - R2 primary class classifier 9. : R² + RC, and an auxiliary super-class classifier 9x IRP - R where p is the dimensionality of the feature representation and K denotes the number of discovered super-classes. For given input æ, we denote the extracted features as z = fo(x), the class logits as gr(z), and the super-class logits as 8 = =g)

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3.1.2 SEMI-SUPERVISED LEARNING.
Our method builds upon the consistency regularization paradigm, as popularized by FixMatch (Sohn et al. 2020). The total loss for the primary SSL is
T.s + Luu
where Lg is the supervised loss on labeled data and Lu is the unsupervised consistency loss on unlabeled data.
Supervised loss.
For a mini-batch B D', we apply a weak augmentation Aw to i; and compute (E(sottmaxx(eeCollAa(lla
L
Σ
yk),
B
where C denotes the standard cross-entropy with the label yi treated class index.
Unsupervised consistency loss. For mini-batch B, C D", we obtain a pseudo-label from weak view and enforce consistency
strong view. Let qj softmax(ge(fo(A. (uy))) and ūj arg max (gj)e. e- We apply strong augmentation A, to the same sample and compute
Lu =
1
Σ
I(max(q;)) 2 CE(softmax 9. (u 0z)
(3)
Bu
where 7 E (0, 1) is confidence threshold. The mask I(maax.Qg))e 7) prevents low-confidence pseudo-labels from contributing to the loss. practice, új treated with stop-gradient.
3.2 SUPER-CLAN CLASS GENERATION
The foundation of our method is structural prior that groups fine- fine-grained classes into meaningful super-classes. We automatically generate this prior from class-name semanties, requiring no manual annotation or pre-trained vision models. More details are explained in A.8
Generation Process. The generation process involves two steps: (1) We convert the names of all Classees into semantic vectors using pre-trained text encoder (e.g., SBERT (Reimers & Gurevych 2019) or CLIP's text encoder (Radford et al. (021)), (2) We then apply agglomerative clustering on these vectors. This bottom-up technique builds dendrogram representing the class hierarchy. By cutting this dendrogram at a level that yields a pre-specified number clusters (K), we partition the C' classes into K super-classes. This process yields deterministic mapping M {1,..., (1...., K}, where ((c) is the super-class index for class c.
Justification. Our approach of generating super-classes is grounded in the observation that mantic relationships captured by pre-trained text embeddings often correlate with visual tax- onomies (Radford et al. 2021). This strategy is particularly advantageous for manual hierarchy creation, which is often infeasible for large-scale or specialized datasets. is important to clarify that the novelty of our work does not lie in this generation process itself, which is straightforward application of existing tools. Rather, we employ this process as simple and effective means to obtain the structural prior required for our main contribution: new, dynamic debiasing mechanism. Our goal is to show that even an approximate hierarchy can alleviate critical issues when leveraged by powerful debiasing algorithm.
it sidesteps the need
Crucially, the effectiveness of our framework does not hinge on any single text encoder. This robust- ness is key finding, shows that our method's principle is general and reliant on specific, potentially expensive text model. SCAD consistently improves performance regardless of the text encoder used, from lightweight GloVe (Pennington et al., 2014)) to large-scale CLIPP This indicates that SCAD's benefit stems from the debiasing mechanism rather than encoder capacity, a conclusion further supported by our ablations in Section|4.5
In this work, obtain super-classes by applying text encoders to class names, but SCAD itself agnostic to the hierarchy source: only requires a mapping from classes to super-classes. The same interface
be instantiated using existing taxonomies manually defined hierarchies. In extremely rare or highly domain comain-specific settings where class names provide a limited semantic signal, ontology- expert-defined super-classes offer natural alternative to text-based grouping.
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3.3 TRAINING PROCEDURE
The feature extractor fa. class classifier ge. and super-class classifier are trained jointly. The goal of this stage is not only train the main classifier, but also to train reliable super-class classifier Is that can provide trustworthy signal for our debiasing mechanism.
The rationale for this joint training hinges on the observation that the auxiliary task of super-class classification is inherently simpler and more robust to data imbalance. This robustness stems from two factors: (1) it is coarse- coarse-grained problem with significantly fewer classes (K < C), and (2) grouping classes often mitigates the label distribution imbalance, noted in prior work (Du et al.) 2023). Consequently, Is can learn to produce reliable predictions even for minority class samples, providing stable signal that is crucial for our debiasing mechanism. As we show in Appendix [A.4. 9s indeed yields a more balanced and reliable prediction distribution on real-workd unlabeled data.
To achieve this, we introduce auxiliary SSL loss for the super-class task, Laperr which mirrors the structure of the primary loss:
Lsuper Susper super
Here, the supervised component, Luuper is the standard cross-entrops loss computed on the labeled batch B; using super-class targets y, yiiipr (y,), where .M maps each fine- ain--griied class to super- class (either from a drtaset-provided hierarchy or from our automatically discovered grouping). The unsupervised component, T.uper is consistency loss computed the unlabeled batch B.,, enforc- ing that the model's super-class predictions are consistent across strong and weak augmentations.
The final training objective is weighted sum of the primary and auxiliary losses:
Lestal L + sasaeer
(5)
where À is a hyperparameter balancing the two tasks. This objective encourages fo to learn repre- sentations that are discriminative at both fine-grained and coarse-grained levels.
3.4 SUPER-CLAS -WAAS LOGIT ADJUSTMENT
This section introduces our framework's core contribution: novel logit adjustment method, Super- logit Adjustment (SCAD), that addresses a critical limitation of the standard LA. Stan- dard LA applies a static, global correction to the logits based on the class prior distribution n:
eLA
E log x,
(6)
While effective against global class imbalance, this super-class-une alaw- aware correction is ill-equipped to mitigate local ambiguities, particularly conflicts arising between classes within the same super-class.
SCAD overcomes this limitation with a dynamic, sample-aware adjustment mechanism, illustrated in Figure 2 The process begins by inferring the coarse gaausee context for each unlabeled sample Ug. We utilize the super-class classifier, Js. to obtain a posterior probability distribution over super- soflmax((5)s This distribution, p( p(k|uj), quantifies the estimated likelihood of the sample belonging to each super-class k. For instance, an image of truck would yield a high probability for the vehicle super-class, i.e., p(vehicle/u, (u)) 1.
classes, pkk(u;)
This inferred context is then used to apply a tailored, super-class-avee aware adjustment. We pre-calculate an adjustment vector A RºC for each super-class k to counteract intra-super-class imbalance within that group. Let C₃ {c .M(c) = k} be the set of all classes belonging to super-class. k. The e-th component of 4 is defined as:
i E Ck: maxeee, 3r,,', if Cr.
Bk.er
(Ax)a
(),ee)) is the relative dominance score class e within its super- class k; based on the sample count estimated frequency 14.c. We estimate ni,e from mixture of labeled and high- high-confidence aseudo-Labeled samples: whenever a labeled example with ground- truth label
where Sx.c =
unlabeled example whose pseudo-label is with confidence above the FixMatch threshold t, is assigned to super-class k. we increment ng,0. These counts are recomputed periodi- cally (once per epoch in our implementation) using the current classifier. The normalization in Eq.
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Table I: Results on CIFARIO-LT and CIFARI00-LT datasets for various algorithms and settings.
CIFARIO-LT
CIFARIOB-LT
100
150
Nu 10
20
N1
500
1500
N1 500
N; 1500
50
N; 150
N1
Algorithm
M1 4000
Mj 3000
M, 4000
M, 3000
M1 400
M₁ 300
M1
400
M1 300
Supervised
61.9
4.mm
0..2
46.9 1.03
25. 2
W/LA
53.3 1.44
70.6 0.21
49.5 1.
67.1 0:78
30.2 1.44
48.7 1:0
26.5 1:51
44.1 1.44
FixMatch
1.1
77.5
62.9 +0,30
72.4=
+0.5
$6.3 1006
40.0
50.7 +:225
7..5
77.8 0.53
73.6
1.0
58,1
1:037
w/ CReST+
76.3 1.0.
78.1 0/43
67.5 1.4.5
73.7 0.334
44.5 1.0
57.4 1011
40.1 1
52.1 1::1
DASO
79.1
1:181
1:034
2:0
w/ DASO Ours
75.1 1.0.0
79.2 1,50
69.0 1.A0
75.8 1.033
50.0 1.00
58.7 10038
44.5 1.0.6
53.2 1.500
FixMatch
75.3
82.0
67.0
78.0
47.3
58.6
53.4
w/ DARP
76.6-
80.8 0.2
68.2 0:004
76.7 1.13
50.5 4:0.8
59.9 2.32
4:04
53.8 2:04
w/ CReST+
1.1
81,1
70.9
77.9
44.0
57.1
40.6
$2.3
DASO
77.9 o:ss
82.5
1:1.0
50.7
55.1 1.72
w/ DASO Ours
81.6 +:.:::222
84.0 0.9
74.5 1.21
82.2 0.14
51.8 +:025
60.5 #021
43.7
+3.7
FixMatch
84.1
1..0
w/ Ours
83.5 0.16
85.5 = 0.09
78.6
83.3 0:00
52.7 20.11
61.8 2:21
45.8 2:0.0
56.4 1
Table 2: Test accuracy of previous LTSSL algorithms and ours under inconsistent class distributions, i... Yt # Yu. on CIFAR-IO-LT and STLIO-LT datasets. The Vi is fixed 100 for CIFAR-1O-LT, while it is set to 10 and 20 for STL10-LT dataset. The best results are in bold.
CIFARIOLIT (y of (w)
STL.0-FT (%. N/A1)
%v.
(uniform)
Tu 1/100 (reversed)
%
N1 500
Ni 1500
Nj 500
NJ 1500
N1 1.50
Ni 450
Nj 150
N: 450
Algorithm
34 4000
Md 3000
M. 4000
M. 3000
M1 100k
Mr 1008
1006
100k
FixMatch
33:0
81.5 =:15::11
62.5 2.00
71.8 1:70
56.1 #:33
22.4=0011
47.622487
64.
w/ DARP
82.5 2:25
$4.6 1:3
70.1 +-122
66.9 1:55
75.6
59.9 29.9
72.3 2.50
CReST
83.2
87.1 1.73
70.7
80.8
61.7 1225
11.61117
57.1
68.6
w/ CReST+
=2.2
86.4 1:42
62.9 1:39
72.9 2:00
61.2 1:22
71.5 6:06
56.0 1..0
w/ DASO
86.6 16.61199
88.8 1898
71.0
80.3
70.0
18.4
65.7 1.17
75.3 1:49
FixMatch + w/ Ours
93.0 2013
93.4-005
86.1 1:0
89.8 19.8
77.8 2:045
83.6
75.8 c:110
82.0 2:35
yields Bk.c [0, 1], making (Ax)e relative penalty and rendering the dynamic term insensitive to the overall dataset size. Since the SCAD correction term P(k: u.y) As a convex combina- also bounded in [0, 1] and thus act as a moderate,
tion of bounded adjustments, its components local correction on top of the global LA term - log whose magnitude can be much larger on highly imbalanced datasets. We empirically found this construction be numerically stable across all benchmarks. For the vehicle super-class, this formulation assigns high penalty (up to 1) to the dominant automobile class and a smaller penalty the minority truck class. For all classes outside this super-class fixed maximum penalty (i.e., the strongest suppression level) is applied to prevent potential confusion. This max-penalty design motivated by prior work (Tao al. 2023).
The final SCAD adjustment is formulated by augmenting the standard LA correction with our sample-aware term. Specifically, we use the inferred super-elass posterior p(klu,, as dynamic weight to create a convex combination of the pre-computed adjustment vectors, As. This yields single, sample-specific adjustment vector. The complete SCAD formulation is:
ESCAD =G- 1-1ogg
log
Σ p(klu))
(8)
This dynamic formulation is the key to SCAD's effectiveness. As illustrated in Figure 2 for an image of truck, the model's high confidence in the vehicle super-class (p( (p(veiicl))u,) ss 1) ensures that final adjustment is dominated by A A This vector applies a large penalty to the competing majority class (automobile) and smaller penalty to the true class (truck), resolving the local am- biguity. In stark contrast, standard LA applies a fixed, global correction that is blind to this local context, making it ill-equipped to handle such intra-super-class conflicts.

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Table Test accuracy on CIFARI00-LT under uniform and reversed distributions.
CIFARIO0-IT (yt # 7v.)
Y (uniform)
Yuu = 1/10 (reversed)
N1 50
Ni = 150
N1 = 50
Ni 150
Algorithm
M1 = 400
M1 = 300
M1 = 400
M1 = 300
FixMatch
45.5 0.71
58.1 0.72
4..3
57.3 + 0.19
w/ DARP
4.95
55.9
00.9
51.8
CReST
43.5
59.2
1.10
56.4
CReST+
43.6 1.60
58.7
39.1
$6.4
w/ DASO
33.90000
61.8
01.9
60.0
w/ Ours
54.0 0.77
62.4
0..9
60.4
FixMatch ACR
57.2 + 0.19
66.7 0.30
5..6
62.9 0.25
w/ Ours
59.1 = 0.25
66.8 0.22
5..4
63.3 0.12
Table 4: Comparison with other LTSSL baselines CIFARI0/100-LT.
Algorithm
CIFAR-IO-LT
CIFAR-100-LT
= Tu. 100
71 100, Yu
71
100, 7/u = 1/100
"i 7h.
FixMatch SAW
77.5 +: 0.65
81.2 1:: 0.68
72.3 1 0.65
50.1 1 0.10 53.1 = 0.16
w/ Ours
79.4 - 0.70
81.1 + 0.30
75.7 + 0.57
FixMatch ABC
81.1 + 1.14
82.7 + 0.40
68.9 + 0.61
53.3 = 0.79 55.1 0.19
Ours
82.0 0.30
83,0 0.42
73.2 1.50
FixMatch COSSL
83.1 + 0.45
88.8 + 0.42
85.1 + 0.58
$3.9 = 0.78
Ours
83.3 + 0.32
89.0 + 0.27
84.8 + 0.47
55.0 - 0.21
FixMatch CDMAD
83.6 1: 0.46
87.5 1: 0.46
77.6 + 0.70
54.3 1 0.44
w/ Ours
85.1 + 0.11
87.9 = 0.10
79.4 = 1.15
54.9 = 0.76
4 EXPERIMENT
We conducted experiments used datasets in LTSSL. including CIFARIO-LT (Krizhessky et al.) 2009), CIFAR 100-LT (Krizhevsky et al., 2009,, and STLI0-LT (Coates et al. 2011). Additionally, we performed experiments on the ImageNet- 127, Food101-LT (Fan et al. 2022) dataset.
Followed by Wei Gan (2023), we used three different settings: consistent, uniform, and reverse. The consistent setting refers when " Yu- The uniform setting indicates that Tu 1. Finally, the reverse setting describes the case where " and 7u have reciprocals of the consistent setting.
4.1 IMPLEMENTATION DETAILS
Following DASO Oh et al. 2022 and ACR (Wei & Gan to various existing methods, including FixMatch (Sohn et al. 2020), FixMatch+DASO, and Fix- Match+ACR. If the base method does not adopt the I.A-based pseudo-label debiasing, apply only super-class learning.
2023) settings, apply framework
We conducted the experiment for the LTSSL method in different experiment settings including SAW (Lai et al. 2022), ABC (Lee et al. 2021). CoSSL (Fan et al. (022). and CDMAD (Lee & Kim 2024) to show that framework
enhance the other method in different setting. For detailed experimental settings, please refer to Appendix A.2 We set the top-1 accuracy as our evaluation metric. All experiments were conducted three times with different random seeds. Please note that the results for ACR differ from those reported in the original pape this discrepancy arises because the authors mistakenly used a different backbone on CIFAR100-1

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Table Test accuracy on ImageNet-122 The best results in bold.
Algorithm/ ImageNet-127
x
FixMatch
29.7
42.3
w/ DARP
30.5
42.5
w/ DARP4cRT
39.7
51.0
w/ CReST+
32.5
44.7
w/ CReST+LA
40.9
55.9
w/ COSSL
43.7
53,9
ACR
57.2
63.6
Ours
60.1
66.7
w/ ACR Ours
60.5
67.0
4.2 RESULTS ON
AND STLIO-LT
In case of Yt- = 7/u To demonstrate the effectiveness of our proposed framework, we conducted experiments by additionally incorporating FixMatch + DASO and FixMatch ACR for comparison. As shown in Table II SCAD enhances performance across almost all settings. shows that SCAD integrates effectively with various existing LTSSL algorithms, yielding complementary effect. In particular, the CIFAR 100-IT performance was impressive with the ACR, where the settings Ni 50 and M1 400 achieved ot-te-of-the-art performance improvements of 2.7% and 2.2% when 20.
T = 10 and N
In case of vi + Tu In the real world, there can be distributional differences between unlabeled and labeled datasets. Therefore, we conducted experiments with both uniform and reverse settings to explore these scenarios.
We observed overall performance improvements when SCAD was applied to ACR in the uniform and reverse settings. Specifically, shown in Table 3 SCAD marginally outperformed DASO on CIFAR100-L1 in both the uniform and reverse settings. Furthermore, combining ACR with SCAD led performance gains of up to 2% and 3.4% in the uniform and reverse settings, respectively.
4.3 RESULTS ON IMAGENET- 127 AND FOOD101-LT
On ImageNet-127 127 Fan et al., 2022), a chal- lenging 127-class subset of ImageNet with severe long-tail distribution (Tu = 286). our method shows a distinct advantage. Following the standard setup with 10% labeled data, our approach outperforms existing baselines, as detailed in Table 5 Notably, it surpasses the strong ACR bascline by a substantial margin of 5.0% 32x32 resolution and 4.0% at 64x64 resolution. This improvement is partic- ularly noteworthy ACR represents highly competitive recent method, highlighting the efficacy of our proposed hierarchical debiasing. Furthermore, when our method is combined with ACR, it achieves new state-of-the-arr results, underscoring its high compatibility as ald--ay module. These significant gains, consistent with results on Food101-L7 (see Appendix [A.6], validate that our approach effectively alleviates the critical problem of intra- sura-super-class imbalance-4 fine-grained challenge that existing methods have struggled to address.
ASS crisso
0.5
15.0
0.50
Tol MOUEARS gldn (14)
12.5
10.0
Angrue
2.5
0.1
0.10
0.00
(0.12)
(145.4
(..9)
Figure 3: Left: figure displays the average confi- dence scores for minority-class samples (8, 9) of the super-class vehicle. Our method effectively reduces confidence in confusing majority classes (0, within the same super-class while increas- ing confidence in the correct minority classes (8, 9). Right: illustration demonstrates that our approach consistently improves accuracy across various LTSSLL frameworks, especially benefit- ing minority classes.
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Table 6: Ablation studies of SCAD on CIFARIO-LT and STLI0-LT datasets.
Ablations
CIFARIO-LT
STLIO-LT
FixMatch
67.8
56.1
Super-class learning
69.2
69.0
+ Aejgtt-djuttmentt (LA)
76.9
70.4
+ Super-class- Aware Debiasing (SCAD)
78.7
71.3
Table 7: Comparison of SCAD with ground truth and various text encoders on CIFARIOO-LT.
Algorithms
FixMatch LA
47.3
w/ Ours with ground truth
50.3
w/ Ours with GloVe
49.7
w/ Ours with SBERT
50.1
w/ Ours with CLIP text encoder
50.4
w/ Ours with eexteebeddling-adae ada-002
50.5
4.4 RESULTS ON OTHER LTSSL BASELINES
Table shows that our framework can also be applied effectively to other experiment settings with state-of-the-ee methods, conducted experiments the three settings of CIFAR10-LT and consistent setting of CIFAR100-LT within the CDMAD (Lee & Kim 2024]. CoSSL (Fan et al., 2022), ABC (Lee al., 2021) and SAW (Lai et al. 2022) framework. According to CDMAD, when we ran experiments with ACR under CDMAD settings, we observed a performance drop from 84.1 81.8 N1 1500 and MM 3000. Our framework remains robust when combined with existing LTSSL in different settings. According to Table 4 when combining CDMAD with our framework, which is another state-of-the-arr method, there was 1.6% improvement in the consistent setting, 0.3% increase in the uniform setting, and 2.3% improvement in the reverse setting. These results demonstrate that our proposed SCAD framework effectively integrates with diverse LTSSL baselines, further validating its utility and applicability.
4.5 COMPREHENSIVE ANALYSIS OF OUR METHOD
Ablation study According to Table 6) we evaluated performance improvements achieved by incrementally adding each component to the algorithms on CIFAR 10-LT and STL10-LT under consistent setting. First, adding the super-class learning component resulted in significant perfor- mance gains, with increase of 2.0% CIFARIO-LT and 23% STLIO-LT. Finally, adding SCAD yielded additional gains of 9.2% on CIFARIO-LT and 1.1% on STLIO-LT. The limited im- provement observed from adding SCAD STL10-LT is likely due to the significant difference in the number of labeled and unlabeled datasets.
Comparison with ground truth super-class labels and various text embedding models As shown in Table 7 there is performance difference between using super-class labels generated from www.mmmmbnn-ndddaa--aoa and using the ground-truth super-class labels provided in 100. We further evaluate SCAD on CIFAR100-L1 using weaker embeddings such as GloVe (Pennington et al. 2014), SBERT (Reimers) & Gurevych] 2019), and still outperform the baseline. This indicates that our approach remains effective even without large-scale models.
(Ryan et al. 2022) or the CLIP text encoder (Radford et al.) 2021),
Minority class accuracy comparison As shown in Figure 3 Our method improves the average confidence of minority classes (8.9) over the LA baseline, while correspondingly reducing the confi- dence of majority classes (0,1) within the same super-class. This result yields consistent gains across diverse LTSSL frameworks, highlighting its general applicability. Moreover, since approach merely appends a super-class classifier trained in a joint manner, entails negligible computational overhead (more detailed in Appendix A.7).

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Hyperparameter settings we define the confidence threshold for class-level predictions as 0.95, following FixMatch. Since super-class learning is expected to converge faster than class-level learn- ing, set the super-class threshold тN to the same value, i.c., T, 0.95, for simplicity and conve- nience. We determined the number of super-classes choosing K and ensuring super-class balance, please refer Appendix A.4
by setting = [C] For a detailed analysis,
CONCLUSION
In this paper, introduce Super-Class-Aware Debiasing (SCAD), novel framework designed to tackle the fundamental challenge of intra-super-clas imbalance in long-tailed semi-supervised learning (LTSSL). Unlike previous approaches that rely solely on global class priors for logit ad- justment, SCAD dynamically adapts the corrective force to the semantic context of each sample. To validate the effectiveness of our approach, we conduct extensive experiments on a wide range of L.TSLL. benchmarks. The results demonstrate that SCAD substantially improves the reliability of pseudo-labels, enhances the learning dynamics of minority classes, and consistently narrows the per- formance gap caused by long-tailed distributions. Moreover, SCAD achieves state-of-the th-t--rrttttttttt results across diverse datasets, outperforming strong baselines and existing debiasing methods. These find- ings highlight that incorporating semantic structure and local class relationships into the debiasing process is a powerful strategy, and establish SCAD as a new standard for robust and generalizable learning in real- world imbalanced settings.
LIMITATIONS AND FUTURE WORKS
Our study mainly evaluates SCAD within LTSSL. Its effectiveness for other tasks such detection or segmentation remains an open question and would require further validation. A promising di- rection for future work is to extend SCAD beyond classification. In particular, applying our idea to more complex tasks such as object detection or semantic segmentation could further demonstrate its utility, as these tasks often suffer from both long-tailed distributions and semantic overlap among categories.
7 ACKNOWLEDGEMEENT
We thank Changhun Kim and Juho Lee for insightful advice and feedback that greatly improved this work. This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (ITP) grant funded by the Korea government (MSIT) (No. RS- 2022-11220311, Development of Goal-Oriented Reinforcement Learning Techniques for Contact- Rich Robotic Manipulation of Everyday Objects (31%); No. RS-2024-00457882, Al Research Hub Project; No.
Artificial Intelligence Graduate School Program (Korea Univer- sity)); by the ITT-ITTC (Information Technology Research Center) grant funded by the Korea gov- ernment (Ministry Science and ICT) (No. ITTP-205-F 2-----22-222---0000 20-22-0000446857) (32%); by the BK21 Four project of the National Research Foundation of Korea; by the National Research Foundation by the
of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2025-00560361 IITP under the Artificial Intelligence Star Fellowship support program (No. IITP-2025-RS-2025- 02304828) (32%) funded by the Korea government (MSIT); and by KOREA HYDRO & NUCLEAR POWER CO., LTD (No.
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A APPENDIX
A.I THE ASSISTANCE OF A LARGE LANGUAGE MODEL (LLM)
This work used a large language model (LIM)) solely for language editing (grammar, wording, and minor typographical corrections) during manuscript preparation.
A.2 EXPERIMENT SETTING
For fair comparison, we integrate our framework with the DASO (hh et al. 2022
ACR Wei
Gan ).23), SAW (Lai et al. 2022), ABC (Lai et al., 2022), ) CoSSI. (Fan al. 2022 and CDMAD (Lee & Kim 2024) algorithms using their official code repositories. For example, we use the code for DASO from httpts::/gggiii
com/yteaaaaaooo com/ytaek ch/daso ceaso.gitt git, for ACR from and for CDMAD from https:///ithhb:
https://githur
ccm htmm/LeeeHuuc/CDNAAD.ooo
When combining FixMatch (Sohn et al. 2020) with our framework, adopt the ACR settings. It is worth noting that the settings for each algorithm exhibit slight differences. For instance, ACR and CDMAD utilize distinct configurations. The ACR setting is specifically tailored generate weakly and strongly augmented versions of input images for use within the FixMatch framework.
Weak augmentation: This pipeline applies random horizontal flipping and random crop- ping with reflective padding preserve the image structure. These transformations designed to introduce minimal changes to the input image while maintaining its semantic content.
Strong augmentation: addition the weak augmentations, this pipeline uses the Ran- dAugment (Cubuk et al.] 2020) method, which applies 2 transformations with 10 magnitude with Cutout Vries & Taylor. 2017) operation.
In CDMAD setting, Weak augmentation same however, there are slight difference in Strong aug- mentation. CDMAD uses RandAugment with 3 transformations of magnitude 4, followed by the Cutout operation.
The hyperparameter settings differ significantly between ACR and CDMAD. In ACR, the labeled dataset batch size is set to 64, the ratio the unlabeled batch size to the labeled batch size set to 1, and the optimizer's learning rate is adjusted using cosine decay schedule. The learning rate at each step is calculated as a cos the current iteration and T is the total number of iterations.
IItt where a is the initial learning rate set to x 10 denotes 16T
In contrast, CDMAD uses a labeled dataset batch size of 32, the ratio of the unlabeled batch size to the labeled batch size is set 2, and learning rate of 2 10-3 All experiments conducted using one NVIDIA RTX 3090 GPU. The software environment includes PyTorch 2.1.2, Torch Vision 0.16.2, and CUDA 12.1.
All settings, We performed the experiments on CIFAR10-LT Krizhevsky et al. 2009). CIFAR100- LT (Krizhevsky al.) 2009). and STI--10 (Coates al. 2011) using the 2
Zagoruyko & Komodakis] 2016) architecture, and on ImageNet-122 127 (Fan et al. 2022). Food101- LT et al. (022) using the ResNet-50 (He et al. 2016) architecture.
A.3 AYTRA-SUPERCILAS--INBALL.
PROBLEM ON CIFARI00-LT
We extended our investigation to CIFAR100-LT to prove the prevalence of intra-super-class imbal- ance. Our results (see Figure 4 show that even on CIFAR-I00-LT, substantial imbalance persists within super-classes when using standard methods such as LA: minority classes inside each semantic group remain underserved despite global prior correction. these intra- ceee-ssuerrclee disparities by explicitly targeting the local bias. These additional findings suggest that intra-super-clle super-elass imbalance not a CiFARIO-specific artifact and that our solution is robust across different long-tailed datasets.
contrast, SCAD consistently reduces
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mioplee
diplos
wingert
panpee
smpuon
(a) FixMatch + LA
(b) FixMatch + Ours
(c) FixMatch LA
(d) FixMatch Ours
Figure 4: Illustration of the intra-super-clast imbalance problem CIFARI00-LT Within the Fig- ure 4a 4b super-class (e.g., camel and elephant), the model frequently confuses semantically related classes, and SCAD effectively alleviates these errors. A similar pattern appears in the Figure 4c 4d super-class (e.g., porcupine and possum), where SCAD reduces misclassifications by addressing the local dominance within the group.
80
100
Clei
79
Imbaiance ratio
Siger dlas
Accuray (%
75
743
Aocursy
78
50
50
77
76
2
5
10
consistent
uniform
reverse
200
400
Scenario
Opoch
(a) Performance for K
(b) Imb-ratio for various scttings
(c) Performance over epochs
Figure 5: Illustration for balancing super-classes and determining the optimal value of K on CIFARIO-LT
A.4 ANALYSIS SUPER-CLASS LEVEL DISTRIBUTION
According Figure 5a we set K [ In addition, in Figure 5b we compare the class-level and superclass-level imbalance ratios of unlabeled data under the consistent, uniform, and reverse settings. Note that in the reverse setting, the reciprocal values are not employed. We observe that the imbalance ratio drops sharply in the consistent and reverse settings, while it slightly increases the uniform setting but still remains nearly identical. Furthermore, owing to this more balanced distribution structure, Figure reports class and super-class level accuracies across training epochs with our method, consistently achieves higher performance at all epochs. These results indicate that the super-class classifier is more balanced and reliable than the class-level counterpart. Experiments on CIFAR 100-LT show a similar trend: According to Figure 6a| the performance reaches its max- imum around K [ and when the original unlabeled imbalance ratio %h is 10 or lower, the performance difference becomes negligible. (See Figure 6b
A.5 VISUALIZATION ON SCAD
To visually demonstrate that our proposed method particularly beneficial for minority classes, we further compare the confusion matrices of FixMatch, FixMatch + Ours, ACR and ACR+Ours. Specifically, as shown in Figure 7 our method significantly enhances classification performance for minority classes under consistent settings, highlighting the effectiveness of SCAD in alleviating class imbalance.
A.6 FOOD101-A RESULTS
Food10 sample 250 labeled and 500 unlabeled images per class, corresponding to imbal-
ance ratios of ?/1 = 7h = 50, 100, followed by CoSSL. According to Table [8] Our method also improves the accuracy Food101-LT about 3% and 7%, respectively.
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51.0
50.5
20
Imbalance Ratio
Accuracy (%)
50.0
15
49.5
49.0
48.5
5
48.0
0
10
20
50
20
30
(a) Performance for K
(b) Imb-ratio for different K
Figure Illustration for balancing super-classes and determining the optimal value of K on CIFAR100-LA
(a) FixMatch
(b) FixMatch Ours
(c) ACR
(d) ACR Ours
Figure 7: Confusion matrices of the test set for FixMatch, FixMatch+Ours, ACR, and ACR+Ours under consistent settings.
A.7 CALIBRATION ANALYSIS ON SCAD
We further evaluate the calibration properties of SCAD compared to FixMatch and LA baselines. We measure calibration using Expected Calibration Error (ECE), which quantifies the discrepancy between predicted confidence and empirical accuracy.FixMatoo exhibits severe miscalibration in tail classes, with ECE values reaching up to 0.59, while SCAD reduces these errors substantially. This indicates that SCAD alleviates underconfidence in tail classes and achieves more balanced calibration across head and tail. We futher evaluated calibration by integrating SCAD into ACR and observed consistent improvements across all frequency groups. The ECE drops from 0.1018 to 0.0517 (Head), 0. 1339 to 0.1228 (Medium), and from 0.1654 0.0926 (Tail). These results show that SCAD not only preserves performance but also substantially enhances prediction reliability, especially for tail classes
Training time analysis As presented in Table 10 the reported time represents the training time per epoch. The difference between our method and the baseline is approximately second, which negligible in practice.
A.8 DETAILED INFORMATION ABOUT THE SUPER-CLAS
We retrieved descriptions for each class name using the Wikipedia API, and for convenience, we only used the first sentence of the returned summary. If corresponding Wikipedia entry ists, we simply use the class name itself. Agglomerative clustering is performed using the clus- tering module from seikit-lcarn. As previously mentioned, we set K
C Specifically, for
CIFAR10-LT (Krizhevsky et al. 2009) and STL10-LT (Coates et al. 2011 K 3, for CIFAR100- LT (Krizevskyy et al.) 2009).
and for ImageNet- 127 (Fan et al. 2022 K 32. The
groupings for CIFARIO-LT, STLIO-LT CIFARI00 100-LT and ImageNet- 127 are detailed in Table [I] and Table These groupings are determined using the rext-embedding-ade ada-00002 model (Ryan et al. 2022).
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Table 8: Test accuracy on Food101-LT with - 50, 100.
The best results are in bold.
Algorithm
7 Y= 50
? = 100
FixMatch
42.6
35.3
DARP
42.0
34.2
w/ DARP + cRT
41.5
34.4
CReST+
43.8
31.2
w/ CReST+ + LA
47.7
36.1
w/ COSSL
49.0
40.4
w/ Ours
51.0
43.5
64
022
Closinde
Figure 8: ECE analysis on CIFARIO-LT
Table 9: ECE comparison between ACR and ACR+Ours
Method
Head
Medium
Tail
ACR 0.1018
0.
0. 1654
ACR+Ours
0.0517
0.1228
0.0926
Table 10: Training time comparison (seconds) per epoch
Method
FixMatch
FixMatch+Ours
ACR
ACR+Ours
Time (s)
28
29
35
36
Table 11: Super-class table on CIFARIO-LT. We use the Wikipedia API to retrieve a summary for given query and perform agglomerative clustering using the lext-embedding-ade ada-002 model, setting number of clusters to
Cluster
Classes
Cluster 0
0: airplane, 1: automobile, 8: ship, 9: truck
Cluster I
2: bird. 6: frog
Cluster 2
3: cat, 4: deer, 5: dog, 7: horse
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Table 12: Super-class table CIFARI00-LTT We the Wikipedia API retrieve summary for given query and perform agglomerative clustering using the text-embedding-atta model, setting the number of clusters 25.
Cluster
Classes
Cluster 0
I: aquarium fish, 26: crab, 43: lobster, 77: snail, 99: worm
Cluster
27: crocodile, 29; dinosaur, 44: lizard, 73; shark, 78: snake, 93: turtlc
Cluster 2
21: chimpanzce, 42: lcopard, 43: lion, 88: tiger
Cluster
orchid, 62: poppy. 70: rose, 82: sunflower, 92: tulip 58: pickup_truck,
Cluster
rocket, 85: tank, 89: tractor
Cluster 5
2: baby, 11: boy. 35: girl, 46: man, 98: woman
Cluster 6
22: clock, 86: telephone, 87: television
Cluster
6: bee. 7: beetle, 14: butterfly, 18: caterpillar, 24: cockroach, 79: spider
Cluster
36: hamster.
mouse. 65: rabbit, 74: shrew, 75: skunk, 80: squirrel
Cluster 9
O: apple, 51: mushroom, 53: orange, 57: pear, 83: sweet.pepper
Cluster 10
5: bed, 20: chair, 25: couch, 94; wardrobe
Cluster 11
23: cloud, 71:
Cluster
33: forest, 49: mountain, 60: plain bottle, 10: bowl, 28: cup 13: bus, 81: streetcar, 90: train
Cluster
Cluster 14
Cluster 15
15: camel, 19: cattle, 31: elephant
Cluster 16
17: castle, 37: house, 76: skyscraper
Cluster
can, key beyboard, 32: flattish, 67: ray. 91: trout
lamp, 61: plate, 84: table
Cluster
Cluster 19
30: dolphin, 95: whale, 72: seal
Cluster 20
8: bicycle, 41: lawn_mower, 48: motorcycle bear, 4: heaver, 34: fox, 55: otter, 97: wolf
Cluster 21
Cluster
12: bridge, 68: road
Cluster 23
38: kangaroo, 63: porcupine, 66: raccoon, 64: possum
Cluster 24
47: maple tree, 52: oak tree, 56: palm tree, 59: pine tree. 96: willow tree
Table 13: Super-class table on ImageNet-127, We use the Wikipedia API to retrieve summary for a given query and perform agglomerative clustering using the cext-mmbedding-dd--00 model, setting the number clusters to 32.
Cluster
Cluster 0
17: electronic equipment, 43: light, 60: camera, 99: magazine, 125: monitor
Cluster
16: furniture, 58: litter, 118: cushion
Cluster
2: jewelry, 22: hairpiece, 36; protective gurment, 4à: gurment, nightwear, 50: uniform, 55: glove, 84: hosiery, 90: gown, 113: dress
Cluster
cleaning.implenent 27: pot, 28: dish, 30; rod, 42: home appliance 71: toilerry, 80 94; cleaning, implement, 106: cooker, 109; tumer athicte, game equipment,
golf equipment, 47: gymnostic apparatus, sports implement, participant, diver 31: frozen, dessert, 37: suace. 39: dip, 63 padding. 74: entree. 77: cracker, III: concoction
Cluster
Cluster
platform, 32; weight, 59:
76: structure, 105: 121: drill.rig
plaything
Cluster 8
15: ceratopsian, 86; mammal. 104; bird
Cluster
25: hus, 46: fure, sled, 98: passenger
Cluster
ray. plactograth, 72: wine, 81: punch. 114: coffee
shark, ganoid, 124: food_fish, 126: fifeefinnd__ish
Claster 11
Cluster
globale, 21: stick, 66: tissue, 83: covering
Cluster
11: fungus. 14: coelenterate. 54: echinodem,, 65: arthropod, 68; mollusk
Cluster 14
88: rescue, equipment, 91: safety helt
Cluster
spaceeraft, 64: rocker, 67: heavier wat.taaaair.aaale 112: lighter_than.ninn oir_crffi
Cluster
18: sailing vessel. 35: ship, our. 119: boat
Cluster 17
6: onguid,lizard 24: teiid 49; lizaddi 78: gocko, 87: agamid, 102: venomous Jizard, 116; iguanid, 122; chamelcon beard.
Cluster
95: hath. Jinen. 103: fabric
Cluster 20
vegetable, fruit, 40: fodder. flower pen.
Cluster 22
3: ligament
Cluster 23
13: frog, 89: smake, turtle. salamander, 100:
Cluster 24
69: cistem, 117: container
Claster 25
D: loaf' of becad. 73: bun
Cluster 26
cup. helmet,
Cluster 27
15: alligator, 62: crocodile
Cluster 28
12: device, 23: tool, 48: sharpener, 57: kit, 85: key, 92: duplicator
military .vchicle
Cluster 30
33: geological formation
Cluster 31
7: demseifishh, 61: fiste 82; scorpanid
17


Reviews:
[{"review_id": "YRzVVzTEn6", "reviewer": "Reviewer_ex6J", "summary": "This paper clearly identifies the limitation of LA being “hierarchy-agnostic” and connects it to real-world intra–super-class confusion; the motivation is clear. It proposes SCAD, a dynamic logit correction framework weighted by the super-class posterior, aiming to introduce semantic context during pseudo-label generation. The approach is simple from an engineering perspective, easy to integrate into existing LTSSL pipelines, and empirically broad. However, in terms of novelty, automatic super-class discovery (text embeddings + clustering) and hierarchical auxiliary tasks are direct applications of existing ideas, and the core dynamic adjustment can be viewed as LA plus a heuristic penalty weighted by the super-class posterior, lacking substantial algorithmic or theoretical innovation and rigorous analysis.", "questions": "Refer to weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The problem is well scoped, emphasizing LA’s failure under local confusion within super-classes; the motivation is intuitive.\n2. The framework is simple and plug-and-play, requiring minimal changes to existing LTSSL methods and is deployment-friendly.\n3. The experiments are wide-ranging, compatible with multiple SOTA pipelines, and show performance gains across datasets and settings.", "weaknesses": "- The biggest issue is limited novelty. It is largely an incremental work without standout contributions and, in my view, does not meet the acceptance bar for ICLR. Super-class discovery and hierarchical auxiliary learning are common ideas in class-imbalanced learning; the core dynamic component is a heuristic penalty formed by LA plus super-class posterior weighting, lacking novel algorithmic design or theoretical support.\n- Since other models are used to partition classes, providing additional information, the performance improvements are expected. The ablation shows a large portion of gains come from adding the super-class task itself; SCAD’s incremental contribution is small on some datasets.\n- The method is also quite constrained because it relies on other models for grouping classes. The datasets used in the paper consist of high-frequency, common categories, where using language models for grouping may work well. If the training set contains fine-grained or rare categories, the results may be significantly affected.\n- The definitions and estimation of Δk and nk,c are unclear. The source of frequencies (labeled/pseudo-labeled/mixed), update strategy, stability, and how the scale is balanced relative to log π are not systematically explained.\n- Sensitivity analysis for K and clustering configuration is insufficient: K=⌈C/4⌉ lacks broader validation; clustering metric, linkage criterion, and text preprocessing details are not fully disclosed, limiting reproducibility.\n- Compared to LA’s Fisher consistency, SCAD’s dynamic component lacks discussion on consistency or optimality of the correction.\n- There are regressions in some settings (e.g., with DASO in certain scenarios), without adequate cause analysis.\n- The code is not publicly available; it is recommended to release reproducible code during the rebuttal period.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CLZYSOXrrl", "reviewer": "Reviewer_Ue4E", "summary": "This paper studies the long-tailed semi-supervised learning (LTSSL) problem, and proposes a new perspective of intra-super-class imbalance. The proposed idea is movitivated by the fail attempt of previous works that leverage logit adjustment for rectifying pseudo-label bias. The authors uncover that the previous works neglect the semantic relationships between classes, thus hindering effective LTSSL. To overcome this limitation, the authors proposes a new framework called super-class-aware debiasing (SCAD), which consists of a super-class-aware logit adjustment. Specically, it leverages pre-trained text encoders such as CLIP to generate super-class labels, and then jointly train a main classifier and a super-clalss classifier. During inference time, it applies logit adjustment according to the prediction results of super-classes. Experimental results demonstrate that the proposed method surpasses most of the previous methods.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The proposed idea is well-motivated. The authors first conduct proper empirical studies to show the existence of intra-super-class imbalance, making the studied problem is meaningful.\n- Considering super-class imbalance for rectifying pseudo-labels is reasonable, which is not proposed by previous works.\n- The experimental results demonstrate the effectiveness of the proposed method.", "weaknesses": "- Although the illustration of intra-super-class imbalance is provided, it is only conducted on CIFAR10-LT. Such results can not demonstrate the universal existence of intra-super-class imbalance on long-tailed datasets.\n- The relationship between super-class imbalance and semi-supervised learning is a bit weak. It seems that the proposed super-class-aware logit adjustment can be directly applied to long-tailed classification. Why must use this method for long-tailed semi-supervised learning?\n- The authors claim that the proposed method lead to negligible computational overhead. However, leveraing pretrained models such as CLIP does lead to additional costs. Such cost should not be neglected.\n- More details should be included regarding how to generate super-class labels using pre-trained models.\n- The performance of SCAD should be highly related to the capability of the pre-trained models, which may affect the quality of super-class labels. Have the authors justified the influence of different pre-trained models?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qx5v0yx9Az", "reviewer": "Reviewer_M8Tz", "summary": "In the paper, the SCAD scheme is proposed to tackle the problem of long-tailed semi-supervised learning (LTSSL). In particular, SCAD assumes access to a super-class inferring mechanism to augment logit-adjustment (LA) by incorporating sample-specific logits correction.The proposed method is conceptually simple, introduces a negligible computational overhead while it can be used in combination with other LTSSL frameworks. Finally, the experiments conducted  across multiple datasets and configurations demonstrate improvements over other SoA methods.", "questions": "Typo (T), Suggestion (S) and Question (Q)\n\nMajor Points:\n\nS1. The caption in Fig 1 suggests that SCAD “resolves” the critical misclassification. However the Fig 1 d. shows similar misclassification trends but less pronounced under SCAD. In this regard, the terms “mitigate” or “alleviate” are possibly better suited here. The same applies for the “resolve” used in L85.\n\nS2. L106-107: “it discovers hierarchies from class names alone”. This claim sounds a bit deceiving as both class names and a pretrained text encoder a utilized to infer the super-classes. Although assuming access to pretrained text encoders is not unreasonable, this has to be more clearly communicated in this context. \nSame argument for L181-182, where the assumption of a pretrained text encoder is omitted.\n\nQ3. Apparently SCAD requires a class hierarchy to work. In the absence of ground-truth hierarchy it is reasonable to seek after a hierarchy approximation mechanism. In the case of the present manuscript, one such mechanism based on a text encoder is used. Is it possible to motivate this particular choice? Why not use a visual encoder instead? Or a combination of these two? \nDepending on the nature of the data and the annotating mechanism, one would expect the relationship among classes to change leading to different class hierarchies. For example, in a dataset where humans are often sitting on bikes, the “human” and “bike” class might cluster in the same super-class. A text encoder might overlook this relationship.\n\nQ4. Do the terms L231: super-class-aware, L237: sample-agnostic and L245: context-specific all refer to the same concept? Please clarify in the text.\n\nQ5: L254: “assigns a high penalty (close to 1)”. Based on the definition of $\\beta_{k,c}$ one would expect in that case the penalty to be exactly 1 i.e., not close to 1. Was the max operator used in the definition of $\\beta_{k,c}$ in L252 meant to be a summation? In that case one could understand the “close to 1”.\n\nS6. Table 1. The selection of the reported algorithms’s performance appears a bit unintuitive.\n- It would be interesting to see the Supervised w/ LA + Ours and/or Supervised w/ Ours in Table 1. This would provide a clearer picture between the interplay of LA and SCAD.\n- L277: FixMatch is used as the baseline which is complemented with different other algorithms. However SCAD is only used in combination w/ DASO. How come FixMatch + Ours is not reported? How come only Fixmatch w/ DASO + Ours is reported and not for example w/DARP + Ours?\n- Similar arguments apply for L280 and L284.\n\nIt would be good to see a clearer motivation on the table construction, which would help readers to better position SCAD. For example, is SCAD competitive as a standalone method or is it a plug and play modification that can be used with other competitive methods?\n\nS7. Table 2. L296: Similar arguments as in S6 apply. Also how come w/ DASO + Ours was not included here? (like it was in Table 1.)\n\nS8. Table 3 L333: Similar arguments as in S6 and S7 apply. For example why FixMatch w/ Ours was used in this case? In contrast to Table 1. where FixMatch + w/ DASO + Ours?\nIt would be good to see a more unified and intuitive scheme of result presentation in Table 1, 2 and 3 that will facilitate readers to better position SCAD in relation to the SoA.\n\nS9. Apparently, any benefit from using SCAD originates from (i) the additional super-class supervision and (ii) the sample-aware delta correction. In this regard, the ablation provided in Table 6 is very useful. However, there is the opportunity here to provide a clearer picture of the interplay between SCAD and LA by also including:\n- FixMatch + LA\n- FixMatch + LA + SCAD\n- FixMatch + SCAD\n\nApparently SCAD involves super-class learning by design. However, could it not be possible to remove the effect of super-class supervision by updating the feature extractor for regular classification while updating the super-class classifier on frozen features?\nBy doing so one would effectively isolate each of the (i) and (ii) and better capture their interplay with plain LA.\n\nQ10. Table 7. suggests that some text encoders give rise to super-classes assignments that (marginally) outperform the ground-truth under SCAD. Are there any further reflections on this? Could it be purely random variation? or does it reflect something deeper?\n\nQ11. It would have been good with a brief discussion on how SCAD affects calibration in the main paper. Section A.6. provides some empirical results on SCAD improving the calibration in tail classes but only in relation to FixMatch/LA. Does this finding generalize when using SCAD in combination with other SoA methods that have been reported earlier?\nIt is important to reflect upon this aspect as the results suggest that the optimal performance is achieved when using SCAD in combination with other SoA methods and one might wonder whether the increased performance comes at the expense of increased calibration error.\n\nMinor Points:\n\nS12. Fig 1 c. and d. Please specify which of the horizontal or the vertical axes are the ground-truth and the predicted class.\n\nS13. Fig 2. might confuse readers as the logits in the super-class classifier do not show up but only the softmax operator. On the other hand the logits do show up in the fine class classifier but not the softmax operator. Unifying the visualizations would help to avoid confusion.\n\nS14. Fig 2 caption L125: The characterization “powerful” see,s redundant. The targeted local adjustment already suffices to indicate a potential improvement over the (base) uniform adjustment. Same applies for “powerful” in L199.\n\nS15. L143: define d. i.e., the dimensionality of the input data?\n\nS16. L144: the c is used to refer to an arbitrary class, while the C is used to refer to the number of classes. This choice can confuse the readers in particular in the presence of typos. For example, L150: is it $l^c$ or $l^C$? From the context (and Fig 2) one might assume $l^C$ but cannot be certain. Also, is classifier $g_c$ anyway connected to the arbitrary class c? From the context one has to assume no. Recommendation is to fix the typos and update the notation accordingly.\n\nS17. L147: define p. i.e., the dimensionality of the feature representation?\n\nS18. related to a S13. L150: Consider including $l^s$ in Fig 2.\n\nT19. L219: Should the source be L_{\\text{super}}?\n\nS20. L223: “ground-truth super-class” might be confusing as this is inferred from the mapping M which can be an approximation of the class hierarchy. Please reformulate to better communicate that M can either be ground truth or approximation leading to “ground-truth super-class” or “approximated super-class”\n\nT21. L246: a blank space is missing after “vector”.\n\nT22. Eq 7 L250: was $\\Delta_{k,c}$ meant to be $\\Delta_{k}$?\n\nS23. L701: The acronym ECE (expected calibration error) was not previously defined.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is generally well-written and easy to follow.\n- The method is well-motivated and conceptually simple (in a positive way) making it easy to use along with other LTSSL frameworks.\n- The conducted experiments convincingly demonstrate the benefits of the proposed method.", "weaknesses": "- Few parts in the paper make somewhat strong claims that need to be softened and better grounded. (see Questions)\n- Despite the improvements, the method utilizes previously explored concepts in the literature known to independently improve performance (i.e., augmenting the concept of LA by super-class awareness). Although it is possible that combining separately explored concepts can provide original insights in the community, one might not find this particular instance to offer much such insights. \n- The main argument to this is that after reading the paper, one might develop only a marginally better understanding of the LTSSL. Namely that utilizing more fine-grained (i.e., sample-aware) logit adjustment improves performance compared to the regular logit adjustment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ddo7vfjRZg", "reviewer": "Reviewer_Rvtp", "summary": "This paper proposes SCAD (Super-Class-Aware Distillation) for class-incremental learning (CIL). \nInstead of treating each class independently, SCAD groups classes into super-classes based on semantic similarity in the embedding space and performs hierarchical knowledge distillation at both class and super-class levels. \nSpecifically, the method first clusters features into super-classes and then aligns both intra- and inter-super-class logits between the old and new models to retain global structure information.\nExperiments on CIFAR-100, ImageNet-100, and ImageNet-1K show improvements over standard distillation-based baselines such as LUCIR, PODNet, and PASS.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The motivation is clear. Existing distillation methods only constrain class-level logits, ignoring the semantic hierarchy among classes, which can lead to loss of global structure.\n\n- The proposed method introduces a simple yet interpretable hierarchical extension, super-class-level distillation, which fits naturally into existing CIL frameworks.\n\n- The experimental setup is standard and fair, covering multiple datasets and comparing with widely used baselines under the same backbone.\n\n- The paper is generally well written and easy to follow.", "weaknesses": "- Core contribution is an incremental extension of LA rather than a new learning principle. SCAD’s main novelty is to contextualize LA with a super-class posterior and precomputed $\\Delta_k$. While the construction is sensible, the paper provides no theoretical guarantees (e.g., consistency/optimality of the dynamic term) and largely positions SCAD as a practical tweak to LA.\n\n- Reliance on pseudo-label–driven frequency estimates is under-specified. $\\Delta_k$ depends on relative dominance scores $\\beta_{k,c}$ derived from counts/estimated frequencies $n_{k,c}$ within each super-class. In LTSSL, such estimates inevitably hinge on pseudo-labels. This paper does not analyze error propagation or provide robustness checks (e.g., how noisy pseudo-labels affect $\\Delta_k$ and whether bias could be re-amplified).\n\n- Design choices inside $\\Delta_k$ lack empirical justification. For classes outside a super-class $C_k$, SCAD applies a fixed maximum penalty (the strongest suppression level). The paper does not compare against alternatives (e.g., soft penalties proportional to inter-group similarity) or show that this choice cannot harm cross–super-class confusions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)