Unlocker: Disentangle the Deadlock of Learning between Label-noisy and Long-tailed Data
Authors: Chen Shu
HongJun Xu
Ruichi Zhang
Mengke Li
Yonggang Zhang
Yang Lu
Bo Han
Yiu-ming Cheung
Hanzi Wang
Venue: NeurIPS 2025 poster ()
Keywords: Long-tail learning
Noisy label learning
Primary area: deep_learning
TL;DR: We propose Unlocker to combine NLL methods and LTL methods to address the problem of distribution deviation in long-tail noisy label learning

Abstract: In real world, the observed label distribution of a dataset often mismatches its true distribution due to noisy labels. 
        In this situation, noisy labels learning (NLL) methods directly integrated with long-tail learning (LTL) methods tend to fail due to a dilemma: NLL methods normally rely on unbiased model predictions to recover true distribution by selecting and correcting noisy labels; while LTL methods like logit adjustment depends on true distributions to adjust biased predictions, leading to a deadlock of mutual dependency defined in this paper.  
        To address this, we propose \texttt{Unlocker}, a bilevel optimization framework that integrates NLL methods and LTL methods to iteratively disentangle this deadlock. The inner optimization leverages NLL to train the model, incorporating LTL methods to fairly select and correct noisy labels. The outer optimization adaptively determines an adjustment strength, mitigating model bias from over- or under-adjustment. We also theoretically prove that this bilevel optimization problem is convergent by transferring the outer optimization target to an equivalent problem with a closed-form solution. 
        Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of our method in alleviating model bias and handling long-tailed noisy label data. Code is available at \url{https://anonymous.4open.science/r/neurips-2025-anonymous-1015/}.

Unlocker: Disentangle the Deadlock of Learning from Label-noisy and Long-tailed Data
Shu Chen', Hongjun Xu Ruichi Zhang', Mengke Li Yonggang Zhang?
Yang Lu Bo Han', Yiu-ming Cheung" Hanzi Wang'
Key Laboratory of Multimedia Trusted Perception and Efficient Computing
Ministry of Education of China, Xiamen University
"College of Computer Science and Software Engineering, Shenzhen University 'Hong Kong University of Science and Technology
'Hong Kong Baptist University {chenshu, xuhongjun] xmu edu. en, luyang@xmu.edt eeceeeumuuedue.e.m edu. en, zhangygdust.nz
Abstract
In real world, the observed label distribution of dataset often mismatches true distribution due to noisy labels. In this situation, noisy labels learning (NLL) meth- ods directly integrated with long-tailed learning (LTL) methods tend to fail due to dilemma: NLL methods normally rely unbiased model predictions recover true distribution by selecting and correcting noisy labels; while LTL methods like logit adjustment depends true distributions to adjust biased predictions, leading to a deadlock of mutual dependency defined in this paper. To address this, we propose Unlocker, bilevel optimization framework that integrates NLL methods and LTL methods to iteratively disentangle this deadlock. The inner optimization leverages NLL to train the model, incorporating LTL methods to fairly select and correct noisy labels. The outer optimization adaptively determines an adjustment strength, mitigating model bias from over- under-adjustment. We also theoret- ically prove that this bilevel optimization problem is convergent by transferring the outer optimization target to an equivalent problem with closed-form solution. Extensive experiments on synthetic and real-works datasets demonstrate the effec- tiveness of our method in alleviating model bias and handling long-tailed noisy label data. Code is available at wthhtps: //gi thub. com. Nonr/henShuu48// ///////nnnokrrr
Introduction
Long-tailed noisy label learning (LTNLL) focuses on addressing the coexistence of long-tailed distribution and noisy labels in datasets. Existing LTNLL methods [ 4 6 are commonly under the potential assumption that the observed long-tailed distribution based on the noisy labels consistent with the true distribution of the clean labels. However, empirical observations from real world reveal that noisy labels can alter the original true distribution, leading deviation between
the observed distribution and the true underlying distribution of the dataset 77] g 9 Due to the diverse real- world noise patterns, the deviations of the distribution are various, typically including three situations: consistent, relieve, and aggravate, as illustrated in Figure la
When addressing this issue of distribution deviation, neither noisy label learning (NLL) nor long tail learning (LTL) can be effective due to deadlock dilemma of mutaul dependency. A mainstream in NLL highly rely on the model prediction select and correct noisy labels to recover clean labels 12 13 [4]. Yet in long-tailed scenarios, model predictions get biased towards head classes,
10
*Corresponding Author: Yang Lu
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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unblased prediction
Number of sammlees
Consistent Aggravate
Methods of
Noisy Label
select correct
distribution
Relieve
Learning (NLL)
distribution
Deadlock
2k-
Methods of Long-tall Leaminng (LTL)
true distribution
unbiased prediction
1k-
rely
adjust
4
6
9
Class index
(a) Distribution deviation
(b) Deadlock dilemma
Figure (a) Three typical scenarios of distribution deviation between the observed distribution and the true distribution. Consistent, relieve, and aggravate denote that the imbalance ratio (IR) of the observed distribution is identical to, lower than, and higher than the IR of the true distribution, respectively. (b) Deadlock between the noisy label learning (NLL) methods and the long-tailed learning (LTL) methods: NLL relies on the unbiased prediction to recover true distribution, while I.T. requires the true distribution to recalibrate the biased prediction, creating circular dependency.
Logit adjustment methods in LTL can recalibrate the biased predictions while relying on the true label distribution 18 [9]. Due to the true distribution deviation, these methods fail to perform accurate adjustments. At this point, we can see deadlock between NLL and LTL, as shown in Figure [b relies on the unbiased predictions to restore a true distribution by selecting and correcting. Conversely, LTL can provide unbiased predictions but relies on true distribution.
posing challenges for selecting and correcting noisy samples in tail classes
[5 16 17
To disentangle the deadlock, we propose a novel method Unlocker based on the bilevel optimization framework. The core idea is jointly estimating the true distribution of the training set and optimizing an appropriate parameter for the adjusting strength. We define the inner problem as training model using NLL adjusted by LTL, and the outer problem optimizing the T based on the inner problem's 's solution. In this framework, model predictions are adjusted using both and the estimated true distribution. This enables the model to better distinguish noisy labels. Improved noisy label selection and correction enhance distribution estimation accuracy. Accurate distribution estimation facilitates 7 optimization, promoting model to reduce bias on the test set. This iterative process continues until convergence, gradually breaking the deadlock. Our main contributions are as follows:
We define new and challenging long-tailed noisy label problem of the deviation between the observed distribution based on noisy labels and the true distribution of the clean labels.
We define deadlock dilemma between the NLL and LTL, where their mutaul dependency renders both methods ineffecvtive.
We propose novel method Unlocker which bases the bilevel optimization to effectively combine NLL and LTL by adaptively optimizing the adjustment strength.
2 Preliminaries
2.1 Noisy Label Selection
Noisy label learning has developed various methods improve model performance by addressing label noise. A mainstream is to select noisy label samples from clean ones and correct the noisy labels [TI 12 13 [4], which has been proven effective in noisy label learning tasks, Specifically. the selection of noisy labels relies on logit-based metrics, which are designed to maximize the discrimination between noisy and clean labels, enabling an efficient separation of the two subsets. Based on these metrics, the original dataset D is partitioned into a clean subset Delean and a noisy subset Dnsisy. Then, the noisy labels in the D Dnoisy are corrected by leveraging techniques such as sumi-supervieed learning (20) 21 The overall training objective is formulated as the total loss ENLL. which is the sum of the clean loss Letean computed on Deden and the noisy loss Lioisy calculated on D Droiayy- Conventionally, the cross-entropy loss is utilized for Ledean to ensure accurate classification on reliable data. For Tenoisys more robust loss functions are often applied to reduce the impact of noise during training [22].

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0.84
Test avg pred
0.30
bu-00.1
b.u
uniform
Test avg pred
0.15
2.0-2.0
tsu-1.
150-15
unifomm
2au-3
3.0
accuracy
0.82
0.06
KL divergence
0.20
tau
accunecy
0.04
divergence
0.10
0.10
0.78
0.76
0.00
0.05
8
1.0
1.5
2.0
2.5
3.0
3.5
0.00
Class index
Class index
Tau values
(c)
Figure 2: (a) Average prediction on CIFAR-10 test set of a NLL method DivideMix [1] adjusted by the true distribution prior IP (y). The training set CIFAR-I0 with imbalance ratio (IR) of and 40% symmetric noise. The model is highly sensitive to the making it prone to under-adjustment or over-adjustment. (b) Test average predictions of models trained using logit adjustment (LA) across different
clean long-tailed CIFAR-IO dataset with IR of 50. As increases, the average predictions transition from skewing toward head classes to uniformity and then to skew toward tail classes. (c) Corresponding test accuracies and KL divergences (between test average predictions and a uniform distribution) for varying т. When 5 2.0 or T = 2.5, the trained model attains the highest accuracy and ouputs the most uniform average predictions.
2.2 Logit Adjustment
Logit adjustment 17
23 19 is an effective technique for long-tailed learning. In standard
classification with classes, the class posterior follows Bayes' theorem P(ylæ=) P(rl))(())
F(z)
Since P(z)) is constant across all classes, it can be omitted. Thus, cross-entropy training yields Pirain (y)=z) x P(xly))Ptrain) (v). where Pt. Prrgen (y) is the class prior training datasets. For balanced
test datasets, a balanced prior Pya( = č is desired, leading to (y](()))) P((lly). Assuming
likelihood P(xly)) is unchanged between the train and balanced datasets, we have the following relationship:
TP Purain (y/æ²) P Pirai (y) x (oftmax(0,(x))) - log irain (y)),
(yer())z)) x
where (z) is the model's logit for class y. To further improve flexibility, a temperature parameter т which modulates the adjustment intensity is introduced, leading the general LA formulation:
arg max softmax (0, vE[C]
log Pimin (v)).
Typically. > is used for post-hoe adjustment, while < can be incorporated as logit adjustment loss during training.
3 Unlocker: Breaking the Deadlock with Bilevel Optimization
3.1 Motivation
To break the deadlock, we first attempt adjust the logits of classical NLL method DivideMix [I] using the true distribution prior Pirain (y). Ideally, incorporating (baseed adjustments leads to unbiased confidence across all classes, reflected by a uniform average prediction on the test set, as depicted by the black line in Figure 2a However, directly employing Irimin (y) for LA results in suboptimal model performance due to under-adjustment or over- [24). Specifically, as Pirain (v) is the same, when the parameter causing the average prediction to skew towards the tail and damaging the head, as shown in Figure 2a Conversely, when biased towards the heads. Without an appropriate 7 to modulate the adjusting strength, even with the precise Puzii (1)) the model could not achieve an ideal unbiased state.
0.5 (red line), the tail classes over-adjusted,
0.1 (blue line), the adjustments are insufficient, remaining the predictions
To further investigate in modulating model bias, we eliminate the influence of noisy label and implement a simple control variable experiment on a clean long-tailed dataset. We test different of (1.0, 1.5, 2.0, 2.5, 3.0, 3.5) with the same IPtvain (y). As shown Figure 2b when 1.0. the tail classes are under-adjusted. As increasing, the test average predictions become more uniform.

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Inner optimization
selection
correction
B14 8: VV
update
LA oe
Dooies
07 (x)
CALL
D
D
selection
Daton
Epoch T
Die
Outer optimization
estimate
x(y)
P(
Or--1
log (2))
div
07:1
- softmax
(0 ((r)
log
(8))
EMA
Die
Epoch T
update
Test
M. K. (P(7) 11
0T+1
Figure 3: The illustration of the proposed method Unlocker based on the bilevel optimization framework, which iteratively disentangle the deadlock between the noisy label learning (NLL) and long-tailed learning (LTL). In epoch I", the inner optimization trains the model & using NLL integrated with logit adjustment LTL. By adjusting logits as (x), it fairly selects and corrects noisy labels, and trains 0. The outer optimization adaptively updates the adjusting strength parameter
modulate model bias towards balance. An EMA strategy is introduced to stabilize the adjustments after the whole optimizations completing. Parameters frozen during optimization denoted by the snowflake (no gradient flow), while trainable parameters are marked by fire.
When 2.0 or r 2.5, the average predictions are closest to uniformity, and the model achieved the highest test accuracy depicted in Figure 2c] When further increasing т, the model gets over- adjusted, and the predictions skew towards the tail. This simple experiment demonstrates that an appropriate is crucial for achieving unbiased model state and influences the test accuracy.
3.2 Bilevel Optimization
Based the motivation, propose Unlocker which utilizes the bilevel optimization framework iteratively disentangle the deadlock. The inner layer optimization trains model using NLL integrating LA, and the outer layer optimizes learnable parameter to regulate the strength of adjustments. Through gradient coupling between layers, this framework maintains model capability against noisy labels while dynamically calibrating class bias.
3.2.1 Inner Optimization
Given long-tailed noisy label training set D {( y:) J the inner layer employs NLL methods integrating LA to train model 0. At epoch T,, for the noisy label selection, an adjustment or (post-hoc) applied to the row logits 0z (x) (gradient- -aaeie to achieve fairer noise recognition, particularly enhancing the recognition accuracy of the tail classes:
(x( = (2) ar-
Based the adjusted logits (x)), selecting metries of NLL methods computed divide 'D into Doteon and D Dooisy- For the noisy label correction for Dnoisg we follow the method in (25) to apply post-hoc adjustment on the generation of corrected labels. With Detean and corrected D Droisys the model training loss L.LL can be computed and the model Or updated at step is given by:
0.+1 0, (eLxLL(O.))
where is the learning rate. Upon completing T' epochs, the optimal inner parameters 07 are fixed as the inner solution for the outer optimization.
Before diving into the outer optimization, two critical components are required addition to support the outer object: (i) the construction of a balanced and clean subset Dbc. and (ii) the estimation of the training set class prior (y).
Construct Dic. During the process of noisy label selection, we further filter the top 4% of samples with the highest confidence of clean as high-purity clean subset. the clean subset follows long-tailed distribution, apply over-sampling and form the final balanced clean subset D

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Estimate iT (yy) As P (3) is unknown in our long-tailed noisy labels problem, we estimate a proxy distribution prior T(y) to approximate it. After the correction for D Drnisy completing, we estimate xt (y) based on the clean labels of the Delean and the corrected labels of the Dnaisy-
3.2.2 Outer Optimization
The outer optimizes learnable parameter to dynamically regulate the adjusting strength to modulate model bias towards balance. The core objective is to minimize the discrepancy between the model's class confidence and uniform distribution. To measure model bias, we leverage the balanced clean validation subset Doe constructed in 3.2.1 Given the fixed inner parameters 01 the model's s logits on Doc are + (I) (gradient- -eadd, and the Tr-regulated prediction distribution is:
P(y z; rT) softmar (0++1 (2) TT loog(T())))
(5)
where log(z) is the fixed class prior term estimated in Sec.
Averaging P(y (;)r)) over Doc yields the class-averaged prediction P(rr) EEW Ply x; TT). leading to the outer objective:
3.2.
min KL (P(rr) IU), U. = C
(Ve E (l....,C|),
where U is a uniform prediction tensor, C' is the number of classes. Minimizing this divergence adapts Ty to drive P(Tr) toward uniformity, forcing balanced class confidence on the test set. The update of Tr at step is derived via gradient descent:
KL (P(T.) IU).
T++1
After completing the optimization of 7r. the adjustments are calculated as @r+1 = Tr+1 log (t). To ensure the stability of the adjustments during the training, especially when the π (1y) fluctuates in the early training, we adopt an Exponential Moving Average (EMA) strategy to update the adjustments. At the 7-th epoch, we update the adjustments as follows:
aT++ = 3 0T+1 + (1 - () OT;
where B is hyperparameter that controls the decay rate of the EMA. 0T+I is then passes into epoch T T to adjust the inner-model training.
3.2.3 Training Overview
As illustrated in Figure 3. through the bilevel optimization, we effectively combine the NLL method and LA to handle long-tailed noisy label training data, breaking the mutual dependency. First, in inner optimization, we perform post-hoc logit adjustment on the selection of the NLL methods, enabling a more fair selection of noisy labels in tail classes. Based on the selection results, we construct balanced clean subset Die and partition the training set into Detenn and Dnoisy- Next, we use the NLL methods to correct the noisy labels in Dnoisy. and estimate the (ty) Then, the model is trained and adjusted using the corrected labels. In outer optimization, the T is optimized to minimize the discrepancy between model preference and uniform distribution based on the Dbe- After the optimization of is completed, update the adjustments through EMA to ensure the stability of the training. The detailed pseudocode the training process can be found in the Appendix A.
3.3 Theoretical Analysis
In this section, we establish the convergence of our bilevel optimization framework by deriving a closed-form approximation for the outer optimization of r. Specifically, we first prove that the outer objective is differentiable and exists global minimum. Then, leveraging the uniform target distribution, we show that the optimal t can be approximated via least-squares minimization, reducing the bi-level problem to normal optimization. This reduction guarantees the convergence of our method. The detailed processes of proof are provided in the Appendix A.2
Theorem (Differentiabiility and Gradient Descent Applicability for Outer Optimization). Under the assumptions that 0* (I) is continuously differentiable with respect to and TT(y) > 0 for all
classes c E (1, C}, the outer objective function J(T) = KL (P(r) || U) continuously differentiable with respect to T. The gradient descent update rule tawa+1 = Tt 7/- V-J(T.) ensures that (T) decreases monotonically along the negative gradient direction.
5

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Based on the Theorem we can further prove the existence of global minimum.
Lemma 1 (KL Divergence Bounds). By the definition of KL divergence, the objective function
J(r) = (P(((T)) establishes the following lower bound and upper bound:
0 S ( log C- H(((T)) S log C.
Theorem 2 (Existence of Global Minimum). Given the continuity of (T) and its lower bound
J(T) 2 and upper bound lim (T) = log C in Lemma 7 and lim J(T) = log C, there exists at least one global minimum point T" R.
In summary. the objective function J(r) bounded and has a global minimum, providing a theoretical guarantee for the optimization process. Leveraging the uniform prior U, we derive a closed-fome approximation for via caas-s minimization of the residual function.
1 Proposition 1 (Least-Squares Closed-Fon Solution). Define pe N Ex Oc(z), the least-square closed-fore form solution for T is:
(log (V) log n7 (M) 111
(9)
(log (v) log 11)²
This 7-8 reduces the bi-level optimization problem to single-level optimization, establishing the theoretical convergence guarantee for our framework.
Experiments
4.1 Experimental Setup
Datasets. We conduct simulated experiments on CIFAR-10/100) 26), including three cases: con- sistent, relieve, and aggravate which we summarize in Figure Ial CIFAR-10/100 contains 50,000 training images and 10,000 test images of size 32 x 32 pixels, where CIFAR-IO contains 10 classes and CIFAR-100 contains 100 classes. To simulate all scenarios, we first construct a long-tailed dataset with an imbalance ratio (IR) by exponential decay. I = Nman/ "man/Nnin represents the ratio of the number of samples of the majority class to the number of samples of the minority class.
Before generating label noise, due to the real-world noise patterns are complex and diverse, we systematically explore the effects of existing noise addition methods on the imbalance ratio of the long-tailed dataset. The detailed analysis results refer to the Appendix A..4. We find that the joint noise hardly changes the original long-tailed distribution in the case of low imbalance ratio, the symmetry noise alleviates the imbalance ratio, and the 12h noise aggravates. Based on these findings, we introduce the joint noise to the clean long-tailed dataset to simulate the consistent case, use the symmetry noise to simulate the relieve case, and inject the 12h noise to simulate the aggravate case. The detailed noise addition methods refer to the Appendix A.3 In the following experiments, the imbalance rate is selected in (10, 50, 100). and the noise rate 1/ which describes the proportion of label noise in the training dataset is set to {0.4, 0.6).
We also evaluate the performance of our method on real-world datasets, including Red Mini- ImageNet [27). Clothing IM (28) and WebVision-50 [29]. Red Mini-ImageNet contains 100 classes with 50,000 training images and 5,000 testing images, annotated via controlled noise protocols. Clothing MM contains 1 million training images obtained from online shopping websites, with 50k, 14k, and 10k images split into clean labels for training, validation, and testing across 14 classes. WebVision consists of 2.4 million images from Google and Flickr, sharing the same 1,000 categories as ImageNet, and includes 50,000 aunottandotated validation and test images. Following the experi- mental setting of [11, we use the first 50-class subset (Web Visions))))50) for training and evaluate on both WebVision validation set and ILSVRC12 validation set for the same 50 classes.
Baselines. We compare our method with the following four categories of methods: (1) Noisy label learning (NLL) methods: including DivideMix [.], UNICON [12] and DPC [4]. (2) NLL methods with post-hoc LA based on real distribution (NLL+LA post-hoc). (3) NLL methods with LA based on real distribution during training (NLL+LA). . (4) Long-tailed noisy label learning methods (LTNLL), including RoLT (1. TABASCO [ and DaSC A. We select the baseline NLL methods to combine with our method. Additionally, for real-world dataset, we further include baselines: MentorNet 30
Co-teaching [10,, HAR [31, UCL [2], RCAL (3), GSS (5).

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Table Test aceuracy (%) comparison of different methods on the CIFAR-100 dataset under varying imbalance ratios (IR), noise types and noise rates involving three scenarios of true distribution shifts. * denotes results from the original papers. Green numbers indicate improvements of our method combined with the NLL method over the original NLL method. Boldface represents the best performance in each case.
Dataset
CIFAR 100
Types
consistent
relieve
aggravale
IR
10
50
100
10
50
"
Joinct. 10%
joent.600%
sym,
+ym.000% sgm, 10%
5gm,10%%
aym.000%
42%
#2f. 20%.
10/56
(21.60%
RoLT II
32.57
21.41
14.76
23.51+
16.61
32.65
22.12
20.25
14108
TABASCO
8
55.31
15.40
40.91
31.28
36.91
26.
45.37
43.60
32.18
17.53
DaSC
36.40
29.59
55.67
39.93
DivideMix
1I|
68.96
62.54
50.75
39.56
45.13
37.15
63.
65.76
51.53
48.68
DivideMix+LAA (post-hoc)
63.53
62.71
32.37
17.53
23.63
11.89
70. 10
68.
52.41
52.68
DivideMix=LA
AivieMMix-Ullocke
71.95
68.97
60.38
53.31
51.26
45.07
72.
69.05
62.59
58, 07
DivideMix
12.99
16.43
19.63
19.13
17.92
111.06
19.39
UNICON 12
63.63
62.42
52.
44.53
46.82
39.83
60.37
58.61
49.60
40.53
UNICON+LA (post-hoc)
67.15
63.13
53. 18
45.27
46.84
40.4
65.41
58.81
50.32
42.78
UNICON+LA
8.53
50.19
67.58
55.
UNICON+Unlocker
69.11
65.53
54.83
62.95
48.38
44.87
65.97
60.49
56.18
44.70
UNICON
15.48
13.11
18.42
11.56
15.04
11.8
16.58
14.17
DPC [4]
70.81
54.91
44.66
34.84
30.43
23.71
70.03
11.32
36.73
1.04
DPC+LA (post-hoc)
69.97
55.13
39.77
30.88
30.59
21.62
70.05
22.85
40.67
2.44
DPC+LA
70.01
50.21
18.16
5:37
5.33
2.38
69.88
61.76
28.36
3.21
DPCHUnlocker
71.86
66.78
57.
50.01
50.18
45.31
72.09
65.78
55.34
49.92
DPC
11.05
111.4
112.53
115.17
110.75
121.60
154.46
118.61
148.88
Table Top and Top test accuracy on We-
Table 3: Test accuracy (%) on Red-Mini-
bvision and ImageNet validation sets. The best results are bolded. The experimental results of other methods are from [5].
Imagenet dataset. The best results are bolded. The experimental results of other methods
from (5)
WebVision
ILSVRC12
IR
10
100
Test
Top-1
Top-5
Top-1
Top-5
?
20%
40%
20%
Standard
80.8
ERM
40.42
31.46
30.88
31.46
Co-tenching
10
63.58
85.20
61.48
84,70
DivideMix
1|]
48.76
48.96
33.00
34,72
MentorNet
30
63.00
81.40
57,80
79.92
UNICON 12
40.18
41.64
31.86
31.12
HAR 31
75.5
90.7
70.3
90.0
HAR 31
46.61
38.71
32.60
31.30
RoLT+ 0
77.64
92.44
74.64
92.48
ULC 2
48.12
47.06
34.24
34.84
RCAL
73.60
TABASCO
50.20
49.68
37.20
GSS 5
83.64
95.86
74.17
95.22
GSS S
52.33
50.91
40.25
36.58
DivideMixxUnnlocker
83.79
96.55
73.21
96.37
DivideMix+Unlocker
53.29
51.48
41.51
37.14
Implementation Details. To ensure a fair comparison with existing methods, we keep the training configurations consistent with the baseline NLL methods. Specifically, ResNet as the backbone architecture. The mini-batch size is fixed at 256. All models are optimized using SGD with momentum of 0.9. A random seed of 123 is used across all experiments to ensure reproducibility. For NLL methods combined with LA baselines, the т is set to 1.0. For our proposed method Unlocker, the learnable parameter is initialized to 1.0 and optimized using SGD with a momentum of 0.9. The initial learning rate for is set to 0.1, which is adjusted to 0.01 at the 150-th epoch. The 3 in EMA update adjustments is set 0.9. All experiments are executed GeForce RTX 3090 GPU using the PyTorch 1.8.0 framework to maintain hardware consistency.
employ the 18- 18-layer
4.2 Results Simulated Scenarios
We conduct a comprehensive performance comparison across three scenarios of the distribution deviations Table [ Our method combines with the NLL methods (NLL+Unlocker) demonstrates superiority over the LTNLL methods under all three conditions. Regarding NLL methods, integrating Unlocker yields substantial accuracy improvements.
CIFAR-100, the combination achieves performance boosts ranging from 1.05% to 54.46%. In contrast, NLL methods directly combined
7

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Table 4: Test accuracy (%) comparison of different methods on the Clothing IM dataset The best results are in bold. The experimental results of other methods are from [S]
Methods
CE
MentorNet
3
Co-tenching T0
DivideMix [T]]
ULC [2]
RCAL+ R
GSS S
DivieMix--Dnlocker
Accuracy
65.42
67.25
67.94
74.76
74.87
74.97
75.83
76.94
DivieeMxx
SiiidMMX Unvoccerr
Divieetice
0.4
30
301
150
200
250
302
Epoch
(a)
(b)
Figure 4: Bias comparison of different models trained on CIFAR-100 (IR=100, sym.40%) (a) KL divergence between the average predictions on CIFAR-100 test set and uniform prediction during training. (b) The average predictions in the last epoch on CIFAR 100 test set.
1.50
1750
7ham diemn uoon tre ning srt morrer buiniig
1.0
6:15
KL divorgence
1220
KL divnggence
250
0.5
6:12
Epoch
Spoch
250
Fpoch
250
(a)
(b)
(c)
Figure 5: (a) The estimated training set distribution the last epoch on CIFAR-100 with an imbalance ratio (IR) of 10 and 12h.60%. (b) The convergence trajectory of the parameter during training. (c) The KL divergence between the average predictions on the balanced clean subset Dre and the CIFAR-100 100 test set. (b) and (c) are conducted on CIFAR-100 with IR=100 and sym.40%.
with true-distribution--ased LA (both post-hoc and training-time LA) often suffer from the problem of over-adjustment, causing training collapse where predictions concentrate single class. Our method adaptively adjusts the parameter 7 during training, effectively alleviating the issues of over- ander-adjustment. This mechanism enables collaboration with NLL methods, jointly addressing the challenges of long-tailed noisy labels data, As a result, our method achieves the best performance among all methods, showing its robust adaptability varying imbalance ratios and noise types. The results of the comparison experiment on CIFAR-10 are presented in the Appendix A.5
4.3 Results on Real-world Datasets
We performed comparative experiments on three real-world datasets validate the robustness of our method in practical setting. As shown in Table DivideMix+Unlockee achieves 83.79% Top-1 and 96.55% Top-5 accuracy on the WebVision-50 validation set, outperforming all baselines. On the ILSVRC12 validation set, it achieves the highest 96.37% Top-5 accuracy and competitive 73.21% % Top-1 accuracy. For Red-Mini- Med-Minn-Imagennt, as shown in Table our method attains 53. 53.29% accuracy under IR=10 and 17220% outperforming SOTA (52.33%) and improving upon DivideMix by 4.5%. Under conditions (IR=100, 17=0%) maintains 36.37% accuracy. On Clothingl M, as shown in Table Table 4 our method outperforms other methods by achieving 76.94% accuracy. These experiments fully verified the ability of our method real-world long-tailed noisy label scenarios, enabling NLI methods to achieve more uniform state against long-tail bias.
4.4 Effectiveness Study and Discussions
Average Prediction Analysis. To validate the effectiveness of our method in reducing models bias to achieve a more uniform state, we conduct experiments by training the models of different methods on CIFAR-100 100 training set with IF 100 and 1) = sym.40% and outputting models' average predictions on the CIFAR-1OW test set. In Figure 4al during training, the KL divergence between the
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test average prediction of the DivideMix and the uniform prediction stabilizes at a relatively high value, as depicted by the orange line. This means the model's average prediction is still far away from uniform, indicating persistent bias of the model. In contrast, by combining with our method Unlocker, the KI. divergence down to approximately 0.08, signifying that the model's test average predictions approach uniformity and the model gets more unbiased. Figure 4b demonstrates the average prediction in the last epoch of the different model. As the orange bars showing. the test average predictions of the DivideMix skew toward head classes, neglecting tail classes. Conversely, our method's prediction (blue bars) distributes evenly across classes. These results collectively validate that our method effectively mitigates soig-talled impact and promotes balancer model state.
Effectiveness of Distribution Estimation. To verify the effectiveness of the distribution estimation module in our method, we conduct experiments on CIFAR- -00 with IR 10 and 12h.60% As shown in the Figure 5a the red line denotes the true distribution of the training set, the orange line represents the observed distribution based on noisy labels, and the blue line is the training set distribution estimated by our method. Notably, the imbalance ratio of the observed distribution is significantly higher than that of the true distribution, indicating that noisy labels aggravate the imbalance ratio of the true distribution. Our method estimates the distribution (blue line) that closcly aligns with the true distribution (red linc), demonstrating its capability to accurately capture the training set distribution.
Convergence of T. We demonstrate the convergence the parameter r for our method trained on the CIFAR-100 dataset with IR 100 and sym.40%. As shown in the [5g our method achieves the stable convergence of the parameter r. Specifically, the optimization of starts after the warm-up of 30 epochs. In the first 100 epochs, the value of 7 decreases rapidly. After 100 epochs, the descent rate slows down, and it converges to a value near 0.48 around 150 epochs.
Effectiveness of Drc- The balanced clean subset Doc serves as a validation set to measure the model bias. Thus it is critical that Dhe can reflect the model's s class confidence. To validate this, we conduct experiments on CIFAR-101 100 with IR = 100 and sym.40%, tracking the average predictions of the model on Doc and the test set at each epoch, By computing their KL divergence, shown in the Figure [c] the discrepancy remains at a relatively low level of approximately 0.12. While there are slight fluctuations, they are in acceptable small range. This result indicates that the average predictions on Doc closely align with those on the test set, demonstrating that Doc reflects model bias.
5 Related Work
Long-tailed and noisy label learning faces challenges in distinguishing clean samples from noisy ones in tail classes. RoLT [ proposes a prototype-based noise detection method using class centroid distances to select noisy samples. ULC [2 combines class- noise modeling with uncertainty quantification to account for cognitive and incidental uncertainties. TABASCO X employs weighted IS divergence and adaptive centroid distance to distinguish. These methods often integrate semi- supervised learning to refine predictions after noise detection. HAR [11 applies heteroscedastic adaptive regularization to high-uncertainty and low-density data points. RCAL 3 leverages unsupervised contrastive learning to eliminate noisy samples and restore representation distributions, enhancing model generalization. Detailed related work on long-tailed learning and noisy label learning individually is provided in the Appendix A.6
6 Conclusion
In this paper, we address the challenging problem of long-tailed noisy label learning where the observed distribution based on noisy label deviates from the true distribution. When addressing this issue, deadlock dilemma between noisy label learning (NLL) and long tail learning (LTL) arises. To disentangle the deadlock and tackle the long-tailed noisy label problem, we propose Unl ocker, a bilevel optimization framework that iteratively optimizes an adjustment strength parameter 7 to effectively combine the NLL methods and LTL methods. Extensive experiments on synthetic and real-world datasets demonstrate that Unlocker significantly outperforms SOTA methods. However, our method has limitations: (i) potential impurity of Dac. and (ii) approximation errors in distribution estimation. Our future research will focus address these limitations.

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Acknowledgements
This study was supported in part by the National Natural Science Foundation of China under Grants 62376233, 62306181 and 62376235; in part by the Natural Science Foundation of Fujian Province under Grant 2024J09001; part by the RGC Young Collaborative Research Grant C2005 by the NSFC / Research Grants Council (RGC) Joint Research Scheme under Grant N HLBB2K 14/21; part by the General Research Fund of RGC under the Grants 12201323 and 12200725; part by the RGC Senior Research Fellow Scheme under the Grant SRFS2224-2S002 and in part by Xiaomi Young Talents Program. YGZ. was funded by Inno HK Generative Al R&D Center.
part
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20

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A Appendix Supplemental Material
A.1 Detailed Training Process
The following is the process of our proposed method Unlocker, which leverages a bilevel optimiza- tion framework. Within each epoch, the inner optimization employs NLL methods with LA train model, while the outer loop optimizes the learnable parameter 5 to dynamically scale the strength of LA. This process adaptively tunes integrate NLL methods and LA, iteratively disentangling the NLL-LTI deadlock and enhancing model robustness against long-tailed noisy label data.
Algorithm Detailed training process of Unlocker
Training data D {( v.) Number of epochs Nepoch, NLL algorithm A, learning ratesy), EMA decay 3
Input:
Output: Trained model 9, Optimized
Initialize: Model Parameters do. Learnalee Parameter To + 1.0, Class Prior a(y) + EstimatePrior(P)) adjustments for epoch = T from to Nepoch do // Inner Optimization for k = to do
To log(n(y))
Dx t- GetBatch((D.))
Inference Mode: Oz -- (T())) oT
>> Post-hoc logit adjustment
end for
Delean: D Doossy Doc + A(67)
Select noisy labels according to A Correct noisy labels according to A Estimate class prior of D
Dnoisy t A(O,, Dnoisy)
(() t EstimatePrior Prio (Delean: Droiey)
for k 1 to do
DR + Det,
0 SOr + aT, Dr C Delean otherwise
logit adjustment
10r,
CNLL < ((0))()),
Compute model loss according A Update model
O1+1 t 02 - (oVNNn
end for
// Outer Optimization for k to do
D + BeetBatch(Dbe..
Inference Mode: fr+ (Dx)
t (r+1 - log π (3)
> Post-hoc logit adjustment Compute outer loss according to Eq. 6 D Optimize
C, KL (ErDD [Er-D_,oftmax softmaxoff/...oomm (Dx)) U)
Tk+1 T 1/7 (TT.(TT)
end for
aT+1 TT+1 log(T()))
Compute adjustments EMA update for adjustments
0'+11 + B ar+1 (1 3) or
end for return 8,
A.2 Theoretical Proof
Proof of Theorem 7 We prove the differentiability of the outer optimization with the following steps:
Step 1: Objective Function Expansion Substituting P(rr) EewD. P(y x; TTTT) into the KL
21

---

divergence J(7) - KL (P(T) U) U. = C. we get:
J(T) = KT. (P(T)) (1
Σ Po(T) log Pc(T)
Up
Baw P(y = e x;
DEnDD P(y = 1; log
1/C
c=1
Din P(y - c k; r; (log De P(y - c. a;; [; + log C)
c=1
EDw Doe
Σ
Ply c x; log DDr Ply c [';r]] log C'
e=l
Eg Doe
Σ
Po log Pe + C,
(10)
where P. Ply x; r) denotes the conditional probability that sample belongs to class under the adjusting of т.
Step 2: Gradient Derivation Taking the derivative of (TO) with respect to T using the chain rule:
C OP.
V+J(T) = Eg Dro Σ Or
(log Pa + 1).
(11)
Given the component log P.+1
Tv( log -k=1= (0x())) +1 differentiable
in r, we focus on analyzing the differentiability of dP., For the softmax function P. Σ c*
dr
with &e = 0c(x)) - F log Ty()), we derive
ap: Br as follows:
oP.
c
Or
Σ E
De
ΣE E
L e
Like t 600
ck= e 8z
Ek=1e0 6t
Oze
dr
Lk=]
e
kk=
8sk
Dr
Σ k=
C
et
Σ Et
Oze
0zb
P.
0T
k=1
Ek=1
-=
e#h
Ør
dzc
C
O2k
P.
Pk
(12)
ØT
k=1
Differentiate 2, = 0x(x)) T log Tz(y) with respect to T, we have Øz,
= log (y( and
Oza
ar
ar
- llg TR. Substituting 0zs dea into (12) we have: Ø7
and
dr
22

---

OP. Pe 0T
log Tc(
Σ
(
Pk (- log (ogggTk))
k=1
Po. -log To(
Pa log Tr
(13)
Given the continuous differentiability of P. and the the constancy of TT(y). the gradient an. or exists and is differentiable. Substituting (13) back into (II), the gradient (r)(T) is thus continuously differentiable, and simplified to:
log log (v) (1
VrJ(F) = Eow Doe
Σpe Po
+
Pk log Th
(log +
(14)
1m
Thus, by the differentiability of (T), gradient descent guarantee convergence to a local minimum.
Proof of Lemma 7
We establish the bounds of J(T) as follows. We first expand the outer optimiza- tion function according the definition of KL divergence:
Pc(T)
J(7) KL (P(T)]IU)
Σ Po(T)
Ue
PP(T)
Po(T) log
E Pc(T) log (Pc(T)-C)
Σ Pc(T) log Pc(T) + log Cl
Σ Pa(T) log Pog log C' Pa(T).
(15)
Note that De Po(T) = (since P(r) is a probability distribution) in the second term. The first
term is the negative entropy Σe c= P((T) log P.(T) =- (P(r)) of P(r). Thus, we obtain:
J(T) log C (P(T))
(16)
Lower Bound The entropy H (P(r)) is maximized when P(r) is uniform, achieving H (P(7)) log C. Therefore:
J(T) log C (P(7)) 2 log log 0.
(17)
Equality holds if and only if P(T) U. Thus, the lower bound of J(T) is 0.
Upper Bound The entropy H (P(r)) 2 for any probability distribution P(r). Therefore:
J(T) log C H (P(r)) < log log C.
(18)
Equality holds when P(r) is a degenerate distribution (i.c., one class has probability I and others 0). Combining these results, we conclude:
0 S [(T) $ log C.
(19)
23

---

Proof of Theorem 2 We prove the existence of the global minimum point from the perspectives of continuity, lower boundedness and upper asymptotic convergence. Given that we have proven the continuity of (()) in Theorem we focus on proving the lower boundedness and upper asymptotic
convergence of J(r).
Lower Boundedness By Lemma 1
J(r) = log C H(P(r)) 2 0 for all E IR.
Upper Asymptotic Convergence As + +o0, let e* arg ming log (y). Then, -T log (c*) dominates, making:
(c(x))= 1-0og (())
f1, 1, ife c¹,
lim +
P(y - cc 2;
lim
(20)
+ -= (0(()))-
0, otherwise.
Consequently, P(T) + Se., Dirac-delta distribution follows:
lim Pa(T)
lim EzzD, Ply c x; +++000
= EEEDDs lim P(y = - x; ++00
ifc {1,
(21)
0, otherwise.
Substituting P(r) into (P((r)), we obtain:
lim H(P(T) = lim
Σ
Pd(T) Po(T)
-6.) c=1
C
lim Pab
log
(22)
For the components in 22 when c', (r) (T) log (c- 0. When + dt -+00 Po(T) = 0, limp Pc(t) log P.(T) 0. Therefore, the addition result the compo- nents is limp(r)-6.- H(P(T)) = O. Substituting H(((T)) + 0 into J(r):
lim ((T) = log C (((())) log +++00
log C.
(23)
As - -0,, define c* arg max, log π (3). Similarly, we have:
lim J(r) = log C. 7---0
(24)
Proof of Proposition The KL divergence KL((Prr) || U)) is minimized when P(r) U, as the KL divergence is neg-negative and zero at exact matching. This requires solving:
1
Ex P(x, 0; r) EE softmax (0(z) log (y)) U, U = (Aa {l...., C}).
(25)
Leveraging the normalized weighted geometric mean approximation 32 25 as:
can be approximated
E, softmax (0(z) r -lg π (v)) ~ softmax (E.,. (0(x) log T (1)l)
softmax (E (0(z)] T log क (y))
2 U.
(26)
Considering the i-th component 26 we have:
1 ((tmmaxE.--,,10((2)))) - (og((())) ~ C (og(m)))-71
(27)
Taking the log of both sides, obtain:
Ex [x(2)] - log 1, Vc.
(28)
24

---

where K = (ex((e (=]] (0))) log
(,))))) (,),,(v))
denotes constant. Choosing class 1 as reference, we subtract the equation for from that of class e:
BewD [0(())] - D Dre [0 (x)) = ((c) log (()).
(29)
Define the residual 29 re = By (0((x)) B, [01 - T -log r()) log π (1)), the least-
squares objective is:
min Σ
2
Σ
(Bg-Dyy (0(())))))))))))))))))))))))(( E (0) - (log log (1)))
(30)
To derive the closed form solution for we define the objective function f(r) Eg (Me μ4 (log (c) - log π (1)))² with ur = EEnDa 1E (x)), Vc. Using the chain rule, we differentiate S(T) with respect to
0f(r) θt
i
Σ 2(p - pn T(log [ log π (1)))
luc - Mn 7(log (c) log (1))
Or
Σ
2(He - #n - - (() log x (1))) (- log (1)))
=
==1
C
= -
Σ
Me #1 Flog (()) - log log) - log π (1)).
(31)
c=1
Setting the Derivative Zero, we obtain:
C
-2 Ue - -1 T(log - log (1))) - log (()) =
Σ
c=1
c
Σ (Ne (log m(c) - (og -
Σ
(log (log
log क x1))) 0.
(32)
c=1
c=1
Rearranging terms solve for T:
Σ (log (cc) - log (())²211>22-1 Σ (me (ole-1i)0e (1) - log (1))
1s Σe (Mo mn) (log log (1))
(33)
Ee log x1))
where ,1S is the final closed-form solution for T.
A.3 Methods Label Noise Addition
Sym. Symmetric noise (Sym) means that for each sample label, we randomly replace it with one of other classes with a fixed probability 7). For a C'-class classification task, given noise rate y,, the original label y is uniformly changed to other classes except y with the probability 7, Specific noise transition matrix: elements on the diagonal are 1), elements on off-diagonal are 1//(C - 1).
Asym. Asymmetric noise (Asym) simulates the real-world label noise structure. It selects "easily confused" class pairs (such as dog ++ wolf) and specifies the transition probability, while the rest remain unchanged. Labels are only replaced between similar classes, and are not randomly mislabeled as other classes. The process of label flipping is related to the quantity of each class. With the noise
rate denoted as y, we establish the following definitions:7.y(x)) = P[ = j|Y = i, = - " when
i = j. Conversely, [z) PÎý j|Y i n- n7. Here, Y and Ý represent the random variables for clean labels and noisy labels, respectively.
25

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orignal Exttroution F $2.0
500
origiaal Dramiuttonn $3.0
500
Oriihall Erttriuution $1.0
syrk 10rs
400-
sgrs 40ra
400-
ogon eora
Late hrequnacy
200
200
200
103
Clans inde
Clack Indine
Claco Indiie
(a) sym.20%
(b) sym.40%
(c) sym.60%
Original Distrbbction F-50.
Or ginal Distrbuttonn F=
onig na Clst Button D
Chourved D dortiation F-
Obsareed Dittiibaiio 2-3.2.
-100
Obearved Stitt Bution F--22 C.
La74 Trequncy
45%
4.00
200
500
200
200
114
300
100-
20
643
100
20
101
1000
0
20
482
100
1.00
(d) 20%
(c) asymm40%%
(f) asym. 40%
original Inttrtaation Oiserved E critaation 18.
500
originall Decritation F- 12.0 Osserred Exerituation F 12.9
Original Extrtbution 11.00
Opserved Eievibutioon 109.
Labe feeourey
400-
Lobe hrooeemo
400-
Trepsanncy
200
200
-00
200
200
100
200
2000
20
(
100
20
40
60
60
105
0
20
-0
60
00
100
(g) joint.20%
(h) j0int.40%
(i) joint.60%
APh
Eipribution
2-
1000
Diatibution 2-226.2
1250
Chservad Dieyfbution Fw-4422.
800
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150
400
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20
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100
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Uloss Ieadion
Unoss In
(i) t2h.20%
(k) 12h.40%
()) 12h.60%
Original Inttitationn 50.0
500
original Bortttttnn----. F- 52. inc 4ate
500
Oriiinal Enttiiuaioon $5..0 nort
Late hrsquncy
anc 2010
400
Prepssenoy
400-
200
200
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25
103
Clace mnre
Clane Indirv
Claco Inctiv
(m) ins.20%
(n) ins 40%
(0) 600%
Figure 6: Changes in the imbalance ratio (IR) of observed distributions (orange line) under different noise types with varying noise ratios, in comparison to the fixed IR of the true distritbuion (blue line). Symmetric (sym) and instance-dependent (ins) noise alleviate IR, while asymmetric noise (asym) may reverse the long-tailed pattern. Joint noise preserves IR at low imbalance. Tail-to-head noise (t2h) exacerbates IR compared to the original distribution.
Joint.
The Joint noise 5 label is generated by the noise transfer matrix, which represents the probability of the clean label flipping to the noise label. Let Y represent the clean label variable, Y represent the noise label, X represent the instance feature, and the transfer matrix T(X = z) is
26

---

defined as (X) = P(Y = j|Y = =i, = z). Specifically, given the noise ratio 17 E [0, 1), is defined as follows:
(xy()= = -== = | V - 1, x x
" nN N-NN
i=j
(34)
otherwise
Here, N denote the total number of training examples and N3 is the frequency of class j. It combines the class prior information in the dataset to set the transition probability, which is more in line with the situation in real-world scenarios where samples are easily mislabeled frequent classes.
T2H. Tail-to-Head noise (T2H) 33 refers to the phenomenon that in the long-tailed data distribu- tion, the tail class samples tend to be mislabeled as the head class samples. The generation of T2H noise mainly includes two steps: separating transferable and tonttransfeeable samples; randomly selecting tail class samples and assigning head class labels to them. We define transition matrix T E RCC where each element Tr, = P(ys = h 1) represents the probability that an instance with the true class L is mis-labeled class h. In the context of T2H noise, the samples from the tail class / have a relatively high probability of being mis-labeled as the head class h, i.e., Te, >> Te (where e' denotes a rarer tail class with fewer samples than t). Meanwhile, the probability that sample from the head class h is mislabeled as the tail class t is very low, Th, z O. Through this transition matrix, the T2H noise quantitatively defined from a probabilistic perspective.
is closely related to the characteristies of each instance and its class label. It is generated by setting random noise rate for each instance, which follows truncated Gaussian distribution, and the noise rate of each class is also randomly set. In this noise model, the probability of label flipping varies for each specific instance. Taking the CIFAR-10/100 datasets as examples, when generating instance-depender noise, for a given clean sample set and a set noise rate n,, random noise rate set for each instance one by one according to the truncated gaussian distribution, thereby realizing the generation of instance-dependent noise.
IDN. Instance-dependent noise (IDN) 13
A.4 Analysis of the Effects of Different Noise Additions Methods on Long-tailed distribution
We systematically investigate the impact of existing noise addition methods on the imbalance ratio (IR) of true distribution when applied to clean long-tailed datasets. The results are presented in the Figure 6 Sym noise alleviates the long-tailed problem because the samples of the head category are evenly distributed to other classes, indirectly balancing the data distribution. Baesd on this finding, we choose symmetric noise simulate relieve scenario. Asym noise induces reverse long tail which means that some tail classes surge in count because head-class samples are frequently mislabeled as them. However, the reverse long-tailed scenario is impractical. Therefore, we opt not to use asymmetric noise in our experimental setup. Joint noise usually refers to the existence of some correlation between the noisy label and the long-tailed distribution. In some cases like long-tailed distribution with low IR, joint noise maintains the original long- tailed structure, keeping the imbalance ratio unchanged. However, in other cases, it may either exacerbate or alleviate the IR, which is uncontrollable. Thus, we only select joint noise to construct consistent scenarios for long-tailed distribution with low IR. T2H noise significantly aggravates the long-tailed problem, and the number of tail-class samples is further reduced, causing the model to be biased towards the head classes. We choose 12h noise to construct simulated aggravate scenarios. IDN noise, like symmetric noise, alleviates IR after adding noise.
A.5 Results on Simulated Scenarios Based on CIFAR-10
We conduct experiments using the NLL method DPC on CIFAR-10 to evaluate the performance of our method under scenarios with varying imbalance ratios (IR=10, 50, 100), noise types (joint, sym, 12h), and noise rates 47=40%, 60%). As shown in Table S DPC combined with Unlocker (DPC+Unlocker) achieves SOTA test accuracies across all the scenarios, outperforming baselines DPC and DPC with direct LA integration. In the consistent scenarios, DPC+Unlocker achieves accuracies of 93.71% 71% and 90.93%. yielding improvements of 0.18% and 5.30% over the original DPC (93.53% 85.63%) respectively the relieve scenarios where tail-class clean sample selection is particularly challenging, DPC+Unlocker reaches significant improvements over DPC, ranging from 11. 11.06% to 19.87%, validating its effectiveness in mitigating long tail induced model bias and restoring the original NLL performance. Under aggravate scenarios, DPC+Unlocker maintains stable gains
27

---

Table 5: Test accuracy (%) comparison of methods on the CIFAR- 10 dataset under varying imbal- ance ratios (IR), noise types and noise rates involving three scenarios of true distribution shifts. Green numbers indicate improvements over the original NLL method. Boldface represents the best performance in each case.
dataset
CIFAR-10
types
aggravate
IR
10
50
100
10
50
joint 40%
joint
sym 40%
5ym 60%
sym 40%
sym
DPC [T4]
93.53
85.63
78.33
57.90
60.07
45.39
93.13
72. 22.10
74.29
70.81
DPC+LA (post-hoc)
74
85.31
78.93
58.05
62.40
40.08
83.82
76.21
75.93
61.12
DPC+LA
$8.31
81.53
75.60
45.77
30.94
36,50
91.27
83.11
61.75
48.40
93.71
90.93
89.39
72.86
73.15
65.26
93.76
92.05
88.67
83.35
10.18
111.06
114.96
113.08
70.63
119.95
114.38
112.54
over 1.04% 19.95% DPC. These results highlight efficacy of Unlocker in disentangling the NLL-LITL. deadlock and enhancing model robustness against long-tailed noisy label data.
A.6 Related Work
Noisy Label Learning (NL). Noisy label learning focuses tackling the challenge of inaccurate supervised label in datasets. It mainly evolves along two directions: noisy label detection and correction, and robust noise label learning. The former mainstream typically uses two-step process: selecting noisy labels via metrics such as loss or divergence, and then correcting them through techniques like semi-supervision learning [T0 out noisy labels, these methods have demonstrated efficiency in both experimental and real-world scenarios. Robust noisy label learning mitigates noise by adjusting loss funetions via regularization to disregard or reduce noise impact.
[4]. By directly selecting and filtering
or noisy transiton matrix 34 35, 36
37
38
Long-tailed Learning (LT). Long-tailed learning is aimed improve the accuracy tail classes caused by skewed datasets distributions. Re-sampling is a classic method, which directly balances the distribution through reducing samples of head or augmenting samples of tail 39 weighting enhances the focus of the model on tail classes by adjusting the sample weights in the loss Ensembling learning improves model performance by aggregating multiple networks within multi-expert framework 45 46 47
Re-
function 42 23
48 49). The two-stage decoupling strategy achieves rebalancing of decision boundaries through the fine-tuning of classifiers [50] 18 511
Logit Adjustment (LA) corrects biased logits by adding offset term to the model's logit [15 16
17 18 19]. Extensive empirical studies have substantiated the efficacy of the LA. Moreover, data augmentation is an effective way to alleviate the scarcity of tail classes by generating tail samples
52 53 54] Besides, recent work such as strategy fusion tailored for multi-objective optimization (MO0) 55 and model parameter space rebalancing (56) also shows promise in balancing model.
28


Reviews:
[{"review_id": "ccKiMocy6B", "reviewer": "Reviewer_uLRe", "summary": "The problem of  long-tailed label-noise learning is considered. This paper introduces \"Unlocker,\" a novel bilevel optimization framework designed to address the \"deadlock\" dilemma in long-tailed noisy label learning (LTNLL). This deadlock arises because noisy label learning (NLL) methods rely on unbiased predictions to correct labels, while long-tail learning (LTL) methods, like logit adjustment, depend on true label distributions to adjust biased predictions, creating a mutual dependency problem. Unlocker disentangles this by using an inner optimization to train the model with NLL methods and LTL methods for fair noisy label selection and correction, and an outer optimization to adaptively determine an adjustment strength to mitigate model bias. The framework is proven to be convergent through theoretical analysis, and extensive experiments on synthetic and real-world datasets demonstrate its effectiveness in alleviating model bias and handling long-tailed noisy label data.", "questions": "Questions\n1. It is well-known that DNNs trained with long-tailed data is prone to give miscalibrated class probability estimates. There are some recent advances from the community of probability calibration showing that calibration in long-tailed classification can be significantly improved by some adaptive label smoothing technique e.g. [CVPR'23]. Have you tried better-calibrated DNNs and will it improve  noisy label selection?\n2. Some important works on noisy label selection are missed in the discussion of related works, e.g. [ICML'20]. \n\nClass Adaptive Network Calibration. CVPR'23.  \nLearning with bounded instance and label-dependent label noise. ICML'20.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths  \n1. The propose of the deadlock dilemma between NLL and LTL.  \n2. The framework is proven to be convergent through theoretical analysis, and extensive experiments on synthetic and real-world datasets demonstrate its effectiveness in alleviating model bias and handling long-tailed noisy label data.   \n3. Code is publicly available.  \n\nWeaknesses  \n1. The requirement of a balanced clean validation set, which may not be available in real-world scenarios. An ablation study of the validation dataset size is necessary.", "was_revised": true, "final_justification": "Thank the authors for the response and addressing my concerns. I decided to maintain the rating of borderline accept.", "rebuttal": ">Q1: The requirement of a balanced clean validation set, which may not be available in real-world scenarios. An ablation study of the validation dataset size is necessary.\n\nThe validation set $\\mathcal{D}\\_{bc}$ is filtered from the long-tailed noisy label dataset, by selecting the top $q$% of samples with the highest confidence in clean labels for each class. The ablation studies on the hyperparameter $q$, which is tied to the size of $\\mathcal{D}\\_{bc}$, are crucial. Under the settings of CIFAR-10, IR=100, sym.60%, and Dividemix+Unlocker, we test the $q$ of {1%, 5%, 10%, 15%, 20%} to explore the impact of $q$ on the $\\mathcal{D}\\_{bc}$ size, purity and model test accuracy.\n| q (%) | 1 | 5 | 10 | 15 | 20 |\n|-----|-----|-----|-----|-----|-----|\n| test acc (%)   |  52.95  |  53.44  |  54.31  |  58.77  |  52.63  |\n|$\\mathcal{D}\\_{bc}$ size |  124  |  620  |  1240  |  1860  |  2481  |\n\nThe results show that when $q$=15%, the model achieves the highest test accuracy 58.77%. At this point, $\\mathcal{D}\\_{bc}$ contains 1860 samples (calculated based on the long-tailed dataset size of 12,406, 12406×0.15=1860). The overall purity of $\\mathcal{D}\\_{bc}$ is 78.32%, with class-wise purity ranging from 37.63% to 100%. A $\\mathcal{D}\\_{bc}$ with high purity, especially when the tail, can better reflect the model bias. By calculating the KL divergence between the average predictions on $\\mathcal{D}\\_{bc}$ and the test set, it is found that the gap between the two is the smallest when $q$=15%, indicating that this $\\mathcal{D}\\_{bc}$ measures the model's bias most accurately, which can effectively ensure the outer tau optimization and inner model adjustment, thereby improving the model performance.\n\nWhen $q$ is too small (e.g., 5% or 10%), the insufficient sample size of $\\mathcal{D}\\_{bc}$ leads to a decrease in the purity of tail classes, an increase in bias estimation errors, and limits the effect of tau optimization. When $q$ is too large (e.g., 20%), due to the inclusion of more noisy label samples, the purity of $\\mathcal{D}\\_{bc}$ drops to 63.2%, resulting in inaccurate bias estimation and a 52.63% decrease in the overall accuracy of the model.\n\nIn summary, when $q$=15%, $\\mathcal{D}\\_{bc}$ maintains both high purity and low KL divergence with the test set bias, ensuring the accuracy of tau optimization. And the proportion of $\\mathcal{D}\\_{bc}$ corresponding to the optimal $q$=15% in the total dataset is within an acceptable low range, reflecting its lightweight and availability in practical and real-world datasets.\n\n>Q2: It is well-known that DNNs trained with long-tailed data is prone to give miscalibrated class probability estimates. There are some recent advances from the community of probability calibration showing that calibration in long-tailed classification can be significantly improved by some adaptive label smoothing technique e.g. [CVPR'23]. Have you tried better-calibrated DNNs and will it improve noisy label selection?.\n\nThank you for your valuable suggestion of using a better-calibrated DNNs to improve the performance of noisy label selection. We conduct experiments integrating the adaptive label smoothing technique on CIFAR-100 under the settings of IR=100 and sym.60%. Results show that integrating this technique can enhance the capability of noisy label selection: the test accuracy of the model using this technique is 40.11%, representing an improvement of 2.96% over the baseline (37.15%) without label smoothing. Additionally, it is important to note that this improvement does not affect the novelty of our core framework. The key contribution of our framework lies in the integration of noisy label methods and LA through bi-level optimization. The noisy label methods within the framework can flexibly adopt different networks and techniques based on specific needs without altering the fundamental design or innovation of the framework.\n\n>Q3: Some important works on noisy label selection are missed in the discussion of related works, e.g. [ICML'20].\n\nThank you for pointing out the omission in the related work. We recognize the significance of this work in noisy label learning. We carefully review this work and supplement the discussion in the \"Related Work\" section of the main text to further improve our coverage of relevant literature."}, {"review_id": "hnTB3PXKzS", "reviewer": "Reviewer_i6r3", "summary": "This paper aims to address the dual dilemma of the noise-label problem and long-tailed distribution. Existing methods often neglect the true distribution deviation and tend to perform inaccurate optimizations. To overcome this limitation, this work proposes a new method called Unlocker to disentangle the deadlock of the dual dilemma. Unlocker consists of an inner optimization that leverages NLL and LTL methods to ensure fair optimization, and an outer optimization that controls the adjustment strength to alleviate the model bias. Theoretical and empirical results demonstrate the superiority of Unlocker.", "questions": "1. In lines 77-78, it is said that $\\tau > 0$ is used for post-hoc adjustment, while $\\tau < 0$ can be incorporated as logit adjustment loss during training. What is the intrinsic reason? And what is the value range of $\\tau$ in practice? Is there any guidance?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1. This paper has a good motivation. Existing NLL methods rely on unbiased predictions, and LTL methods rely on true distributions. Therefore, the combinations of these two methods indeed cause a deadlock. The authors have adequately supported their motivations by providing detailed explanations and empirical results.\n2. Different from previous works, this work proposes both inner optimizations and outer optimizations. It seems that previous works can be integrated into the inner optimizations of this work, while the outer optimizations can ensure the accuracy at a high level. This framework can also motivate more generalizable works in future research.\n3. Both theoretical and empirical analyses are provided to support the proposed framework. Moreover, the performance gains on popular datasets are constructive. The authors have conducted additional analyses to validate the effectiveness of different modules and hyperparameters.\n\nWeaknesses:\n\n1. It seems that the inner optimization objective and the outer optimization objective are introduced separately. What is the jointly optimized objective? Or could you provide the combined optimization procedure or pseudo-code?\n2. In theoretical analysis, the approximation for $\\tau$ can be solved using the least-squares method. However, in experiments, the parameter $\\tau$ still requires adjustments. Are there any potential explanations?\n3. The distribution estimation plays an important role in the problem. Are there any quantitative metrics for evaluating the effectiveness of distribution estimation? Or how to compare the effectiveness with previous estimating methods?", "was_revised": true, "final_justification": "The authors have addressed my concerns properly. I would prefer to maintain my positive score.", "rebuttal": ">Q1: It seems that the inner optimization objective and the outer optimization objective are introduced separately. What is the jointly optimized objective? Or could you provide the combined optimization procedure or pseudo-code?\n\nWe appreciate the reviewer’s valuable suggestion, as clarifying the jointly optimized objective is crucial for understanding the design of our bi-level framework. We will incorporate this clarification in the revised manuscript.\nThe core of our joint optimization objective is to train a model that achieves balanced state across classes while being robust to noisy labels. Formally, the combined objective can be expressed as:\n$$\n\\min_{\\tau} KL\\left(\\overline{P}(\\tau) \\big\\|\\ U \\right) \\quad \\text{s.t.} \\quad \\theta^* = \\arg\\min_{\\theta} \\mathcal{L}_{NLL}(\\theta; \\tau),\n$$\n\nwhere $KL\\left(\\overline{P}(\\tau) \\big\\|\\ U \\right)$ is the Kullback-Leibler divergence between the class-averaged prediction over the balanced clean subset $\\mathcal{D}\\_{bc}$ and the uniform distribution $U$, , serving as the outer optimization objective to balance the model by updating $\\tau$; $\\mathcal{L}_{NLL}(\\theta; \\tau)$ denotes the noisy label learning loss incorporates logit adjustment with parameter $\\tau$, , acting as the inner optimization objective to train a balanced model robust to noisy labels from long-tailed noisy label data.  \n\nRegarding the detailed optimization procedure and pseudo-code, due to space constraints in the main text, we have included them in the supplementary appendix. We will add a clear cross-reference in the main text.\n\n>Q2: In theoretical analysis, the approximation for tau can be solved using the least-squares method. However, in experiments, the parameter tau still requires adjustments. Are there any potential explanations?\n\nIt is an insightful question regarding the theoretical approximation and experimental optimization of $\\tau$.\nThe core role of the theoretical derivation of $\\tau$ via least squares is to prove that our bi-level optimization can be equivalently transformed into a standard optimization problem with a closed-form approximate solution $\\tau^{LS}$, providing theoretical guarantees for convergence of the bi-level optimization.\n\nAlthough a closed-form approximation $\\tau^{LS}$ is derived, the employment of bilevel optimization for $\\tau$ is necessary due to the practical limitations of $\\tau^{LS}$ and the adaptive advantages of the bilevel paradigm.\n\nFirst, the closed-form solution $\\tau^{LS}$ is inherently an approximation, relying on idealized assumptions of perfect estimation of biases in the balanced clean subset $\\mathcal{D}\\_{bc}$ and the accurate approximation of the true class prior $\\pi(y)$. In real-world scenarios, such ideal conditions are rarely met. $\\mathcal{D}\\_{bc}$ may contain residual noise, and $\\pi(y)$ estimation is prone to errors due to label corruption, rendering the closed-form solution suboptimal.\n\nSecond, the gradient-based update of $\\tau$ in bilevel optimization enables an  iterative search that adaptively accommodates practical imperfections. It dynamically tracks the optimal $\\tau$ that evolves with the model’s learning process—critical for handling non-stationary training dynamics, where the model’s bias and noise distribution change as training progresses. However, the computation of the closed-form solution $\\tau^{LS}$ is stationary.\n\nExperimentally, we verified that the accuracy of bi-level optimization on CIFAR-100 (IR=100, sym60%) is consistently 1.8–2.5% higher than that of direct closed-form $\\tau^{LS}$ computation, confirming that while retaining a theoretical foundation, it better aligns with practical training dynamics.\n\n>Q3: The distribution estimation plays an important role in the problem. Are there any quantitative metrics for evaluating the effectiveness of distribution estimation? Or how to compare the effectiveness with previous estimating methods?\n\nThe quantitative evaluation of distribution estimation is indeed critical for validating this component.\nTo address this, we use the KL divergence between the estimated distribution and the true dataset distribution as a quantitative metric, tracking it throughout training. Experimental results show this value decreases to a low range from 0.217 to 0.348 during training, indicating our estimation relatively captures the underlying distribution well.\n\nRegarding comparisons with previous estimating methods, a related long-tailed noise label learning work [AAAI2025] also estimates distributions using clean labels and corrected pseudo-labels. Our method achieves a lower KL divergence compared to it (0.231–0.453).\n\nRobust Logit Adjustment for Learning with Long-Tailed Noisy Data.[AAAI2025]\n\n>Q4:In lines 77-78, it is said that $\\tau$>0 is used for post-hoc adjustment, while $\\tau$<0 can be incorporated as logit adjustment loss during training. What is the intrinsic reason? And what is the value range of τ in practice? Is there any guidance?\n\nThe theory stems from the long-tailed leanring work LA [ICLR2021].\n\n1. $\\tau$>0 for Post-hoc Adjustment:\nWhen $\\tau$>0, it acts as a scaling factor applied to logits after training to rectify class imbalance. This is typically done by amplifying the logits of tail classes. The key advantage is that it does not require retraining the model and can be flexibly tuned for deployment scenarios. \n\n2. $\\tau$<0 for Training-Time Logit Adjustment:\nWhen $\\tau$<0, it is incorporated into the loss function as a penalty term. This penalty forces the model to allocate more attention to the tail classes during training, thus balancing classes. \n\nIn practice, the $\\tau$ is typically set as 1. However, if the adjustment effect is not sufficient, the value of $\\tau$ can be appropriately increased or decreased according to the actual situation. If the model shows insufficient adjustment, we may increase $\\tau$ to enhance the strength of the adjustment. Conversely, if the model shows over - adjustment or instability, we may decrease $\\tau$.\n\nLong-Tail Learning via Logit Adjustment.[ICLR2021]"}, {"review_id": "70X5JopOGX", "reviewer": "Reviewer_6uZ6", "summary": "The authors present a bi-level optimization framework for learning balanced predictors on noisy data. They demonstrate the tractability of the problem theoretically and show strong results against baselines in real-world and synthetic datasets.", "questions": "1. In table 3, Unlocker is incorrectly highlighted as the best result when GSS outperforms it.\n2. Why are LTNLL baselines included in the synthetic results but not the real datasets? And why not the NLL-only baselines in the synthetic? \n3. The flowchart in figure 3 does not provide a clear explanation of the method, specifically in the selection and correction steps. A clearer description may help readers understand the novelty of the work.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "# Strengths\n1. The paper tackles a challenging and relevant problem.\n2. The empirical results are promising in some settings.\n3. The method introduced is a novel combination of LA and NLL which outperforms naive combinations.\n\n# Weaknesses\n1. The paper is not clearly written, and the clearest explanation of the method (the algorithm) is relegated to the appendix. The dependence of the NLL algorithm on the LTL algorithm is nuanced and should be explained more carefully.\n2. The results are not dramatically better than SOTA methods on real-world datasets. More could be done to emphasize where Unlocker is strongest (when noise dramatically alters the distribution, perhaps)", "was_revised": true, "final_justification": "The authors have addressed the main concerns presented by myself and other reviewers. I believe that this work addresses an important problem, but struggles to get that importance across to the reader.", "rebuttal": ">Q1:The paper is not clearly written, and the clearest explanation of the method (the algorithm) is relegated to the appendix. The dependence of the NLL algorithm on the LTL algorithm is nuanced and should be explained more carefully.\n\nWe agree that clarifying the dependence between the NLL and LTL is very crucial. We provide a detailed explanations as suggested below and will revise the main text to enhance clarity.\n- NLL's dependence on LTL: NLL requires LTL to rectify biased logits caused by long-tailed distributions. To be specific, in noise label selection, NLL fixes model parameters to output logits to compute metrics distinguishing noisy and clean labels. In long-tailed scenarios, these logits are skewed towards head, leading to inaccurate metrics and poor noise selection performance for tail classes. Thus, NLL needs LA (logit adjustment) from LTL to modify the logits. Based on the adjusted logits and metrics, the dataset is partitioned into Dclean and Dnoisy. Similarly, in noise label correction, NLL methods generate pseudo-labels for Dnoisy by outputting logits, which needs to be adjusted by LA. \n- LTL's dependence on NLL: LTL depends on NLL for dataset distribution estimation and $\\tau$ optimization. Due to the disruption of noisy labels, the dataset distribution shifts. Thus, LTL requires NLL to correct noisy labels to restore the true distribution. In addition, the outer optimization of $\\tau$ relies on NLL to construct the small balanced clean subset $\\mathcal{D}\\_{bc}$ to update $\\tau$.\n\nRegarding paper clarity,  we acknowledge the need for improvement. Due to space constraints in the main text, we place detailed algorithm pseudocode in the appendix. To improve the clarity of our method, we provide a training overview in Section 3.2.3 to summarize the core procedure. We will revise Section 3.2 by integrating algorithm pseudocode of the method's key  steps into the main text and provide more detailed explain to enhance readability and clarity.\n\n>Q2:The results are not dramatically better than SOTA methods on real-world datasets. More could be done to emphasize where Unlocker is strongest (when noise dramatically alters the distribution, perhaps)\n\nAddressing long-tailed noisy label in real-world scenarios presents extraordinary challenges, as natural noise distributions exhibit complex, non-uniform patterns. In addition, our framework neither introduces additional auxiliary prior knowledge (e.g., dataset prior distributions, dataset noise label rates, or pre-trained models beyond standard backbone networks) nor increases much training computational cost. Under these strict constraints, our framework still achieves performance gains on real long-tailed noisy datasets. Furthermore, the effectiveness of our framework will improve as it integrates newly advanced noisy label methods.\n\nNotably, our method demonstrates significant advantages in cases of severe distribution shifts. As shown in the detailed results in Table 1 of Section 4.2, our method substantially boosts the performance of noisy label methods on long-tailed noisy label datasets, with a maximum improvement of 54.56%. This validates that our method enables effective adjustments of model bias and leverages the capabilities of noisy label methods. \n\nIn summary, although our method does not lead across all real-world benchmarks, it achieves performance improvements in challenging long-tailed noise scenarios—especially those with distribution shifts—without requiring additional resources or prior knowledge. This highlights its robustness and practical value in real-world applications.\n\n>Q3: In table 3, Unlocker is incorrectly highlighted as the best result when GSS outperforms it.\n\nWe apologize for the mislabeling of the best result in table 3. We will correct this annotation to reflect the accurate result in the main text. Thank you for pointing out this problem.\n\n>Q4: Why are LTNLL baselines included in the synthetic results but not the real datasets? And why not the NLL-only baselines in the synthetic?\n\nWe have included comparative experiments between the LTNLL baseline and NLL-only baselines in both simulated datasets and real-world datasets. The details can be found in Section 4.2 and Section 4.3 (see Table 1 for results on simulated datasets and Table 2, 3, 4 for results on real-world datasets). In the revised version, we will more clearly emphasize the two different baselines to enhance readability and clarity.\n\n>Q5: The flowchart in figure 3 does not provide a clear explanation of the method, specifically in the selection and correction steps. A clearer description may help readers understand the novelty of the work.\n\nThank you for your valuable suggestion on improving the clarity of the noise label selection and correction section in the method figure. A detailed explanation of this part is crucial for readers to understand the underlying noisy label methods. Specifically, the process of noisy label methods involves the following steps: First, the model outputs logit for each sample. Different noisy label methods calculate their respective metrics for distinguishing noisy labels based on these logits. These metrics aim to maximize the differentiation between noisy and clean labels, using criteria such as loss and JS divergence. Based on these metrics, the original dataset $\\mathcal{D}$ is divided into a clean subset $\\mathcal{D}\\_{clean}$(containing samples with high confidence in clean label) and a noisy subset $\\mathcal{D}\\_{noisy}$(samples identified as potentially noisy label). Second, for correcting noisy labels in $\\mathcal{D}\\_{noisy}$, noisy label methods typically adopt semi-supervised learning, utilizing the model's predictions to generate corrected pseudo-labels which replace the original noisy labels. We will enhance the descriptive content in the main text's caption to improve clarity of explanation."}, {"review_id": "9CWjdrk1Xt", "reviewer": "Reviewer_wJKt", "summary": "This paper addresses the problem of training with both long-tailed and noisy-labeled data, a setting commonly encountered in real-world applications but rarely studied jointly. The authors identify a mutual dependency---or ``deadlock''---between noisy label learning (NLL), which relies on unbiased model predictions, and long-tail learning (LTL), which relies on an accurate class prior. To overcome this, the paper proposes \\textit{Unlocker}, a bilevel optimization framework: the inner loop applies NLL enhanced with logit adjustment, and the outer loop adaptively optimizes the adjustment strength parameter $\\tau$. The method is theoretically justified with convergence proofs and empirically validated on synthetic and real-world datasets.", "questions": "1. How sensitive is the method to errors in the selection of the clean balanced subset $D_{bc}$? Could small amounts of residual noise significantly affect the outer optimization?\n2. What is the computational overhead of the bilevel optimization in practice? Could the method scale to larger models or datasets like ImageNet-1k?\n3. Have the authors tried Unlocker with modern vision backbones (e.g., Vision Transformers, EfficientNet)? How well does it generalize beyond ResNet?\n4. Could you show an ablation analysis for the EMA component or the effect of fixing $\\tau$ vs. learning it?", "limitations": "yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "This paper addresses an important but under-explored challenge of jointly handling label noise and long-tailed distributions. The proposed bilevel optimization framework, Unlocker, is conceptually sound, novel, and supported by strong empirical results. However, the method is only evaluated on traditional architectures like ResNet and relies on a clean balanced subset whose reliability is not deeply examined, raising concerns about generalizability. While the core idea is promising, broader evaluation and deeper analysis would strengthen the paper.", "was_revised": true, "final_justification": "The author has fully cleared my doubts about the paper.", "rebuttal": ">Q1: How sensitive is the method to errors in the selection of the clean balanced subset $\\mathcal{D}_{bc}$? Could small amounts of residual noise significantly affect the outer optimization?\n\nA small amount of residual noise in $\\mathcal{D}\\_{bc}$ barely affects the model bias measurement and thus does not interfere with the performance of the outer optimization. To validate this concern, we supplement empirical experiments from two perspectives: the purity of $\\mathcal{D}\\_{bc}$ and its ability to measure true model bias. The experiments are conducted on CIFAR-10, IR=100, sym. 60%, integrating noisy label learning method DivideMix, summarized as follows:\n\n- **High purity of $\\mathcal{D}\\_{bc}$:** The overall purity of $\\mathcal{D}\\_{bc}$ maintains 78.32%-98.13% during training. The high purity stems from the construction of $\\mathcal{D}\\_{bc}$ , which selects high-confidence clean-label samples across all classes. While minor residual noise in $\\mathcal{D}_{bc}$ persists, its noise rate remains extremely low, within an acceptable and negligible range, ensuring a reliable foundation for bias measurement.\n\n- **Accurate measurement of model bias:** Even with minor residual noise, the model bias measured by $\\mathcal{D}\\_{bc}$ remains highly consistent with the true bias estimated on the test set, confirming the accuracy of $\\mathcal{D}\\_{bc}$ in measuring model bias. We tracked the KL divergence between bias distributions on $\\mathcal{D}\\_{bc}$ and the test set throughout training. The metric remains stably low (0.05-0.13), confirming that $\\mathcal{D}\\_{bc}$ captures the model's true bias.\n\nBased on the accurate model bias captured by $\\mathcal{D}\\_{bc}$, the outer optimization adaptively updates $\\tau$ to adjust model bias. In summary, our method demonstrates robustness and insensitivity to the minor residual noise in $\\mathcal{D}\\_{bc}$.\n\n>Q2: What is the computational overhead of the bilevel optimization in practice? Could the method scale to larger models or datasets like ImageNet-1k?\n\nThe proposed bi-level optimization introduces minimal additional computational cost compared to standard noisy label learning methods, and can be scaled to larger models or datasets. The core extra operations include: (1) outputting logits for $\\mathcal{D}\\_{bc}$ (accounting for approximately 5% of the total training data) with fixed model parameters; (2) computing losses based on these logits and update $\\tau$. \n\nWe conduct experiments using the noisy label learning method DivideMix and the benchmark CIFAR-100 under the settings of IR=100 and sym.60%, with computations performed on an NVIDIA GeForce RTX 4090 GPU. The results show that our framework increases the total training time by only 0.1–0.3%: the vanilla DivideMix takes 128 minutes, while DivideMix+Unlocker requires 130 minutes—an overhead that is practically negligible.\n\nRegarding scalability, we validate our method on ImageNet-1K, using DivideMix as the baseline, performing on an NVIDIA GeForce RTX 4090 GPU. The computational overhead is consistent with observations on smaller datasets: when training with ResNet-50 on ImageNet-1K, DivideMix+Unlocker takes 127 hours, representing a mere 0.3% increase compared to the DivideMix (123 hours). Additionally, the model performs well on ImageNet-1K, achieving an accuracy of 77.2%, confirming its effective scalability to large datasets. We will include detailed experimental results on ImageNet-1K in the revised manuscript.\n\n>Q3: Have the authors tried Unlocker with modern vision backbones (e.g., Vision Transformers, EfficientNet)? How well does it generalize beyond ResNet?\n\nOur current experiments primarily use ResNet with modern vision backbones such as ViT excluded to ensure fair comparisons with existing SOTA methods under consistent benchmark settings.\nHowever, the idea of combining more advanced vision backbones with our Unlocker framework is very valuable. We supplement experiments using ViT-Base (patch16×224) and EfficientNet-B0 as backbone, replacing the ResNet-18 in the traditional noisy label methods. The results on CIFAR-100 under the setting of IR=100 and sym.60% are as follows: \n- ViT-Base: Achieves 34.88% top-1 accuracy on CIFAR-100, which is lower than ResNet-18 (45.07% accuracy). This performance gap can be attributed to the inefficiency of ViT on small-scale datasets like CIFAR-100. ViT's architecture typically requires large amounts of training data to fully exploit its representational capacity.\n- EfficientNet-B0: Reaches 55.38% on CIFAR-100. Its compound scaling enables efficient learning of discriminative features, outperforming ResNet-18 (45.07% accuracy) by 10.31% on CIFAR-100.\n\nCurrently, our framework is mainly integrated with traditional ResNet-based noisy label methods, but these experiments demonstrate that our framework can also incorporate other advanced architectures, confirming its general applicability and effectiveness.\n\n>Q4: Could you show an ablation analysis for the EMA component or the effect of fixing $\\tau$ vs. learning it?\n\nThank you for suggesting ablation studies on the EMA component and the $\\tau$ adjustment mechanism. It is crucial to validate these module functions and provide empirical systematic support for our framework.  We supplement ablation experiments on CIFAR-100, IR=100, sym.60%, integrating noisy label learning method DivideMix, summarized as follows:  \n**Ablation study on the EMA component**\n\nThe EMA component plays a stabilizing role in the early training phase: it smooths the estimation of dataset distribution, mitigates excessive imbalance in the estimated distribution, and precludes training collapse caused by aggressive adjustments. Experimental results validate this function:  \n- Without EMA, the distribution estimation fluctuates drastically at epoch 16, with the imbalance ratio surging to 1248 (against the actual imbalance ratio of 100) due to the instability of the model's estimated labels. This causes excessive over-adjustment of the model, leading to a drop in training accuracy to 11% with no recovery.  \n- With EMA, the imbalance ratio of the estimated distribution stabilizes within the first 30 epochs, ensuring gradual and controllable training adjustments.  \nNotably, the function of EMA diminishes in the late training phase, when the distribution estimation stabilizes.  \n\n**Ablation study on the $\\tau$ adjustment mechanism**\n\nWe compared the performance of fixed $\\tau$ versus our $\\tau$ optimization strategies:  \n- With fixed $\\tau$=1, the adjustment strength is excessive, causing the model to over-focus on tail classes. This leads to a sharp decrease in head class accuracy, resulting in a collapse in model training where overall accuracy drops to 10% or lower.  \n- With adaptive optimization of $\\tau$ during training, the model reaches an overall accuracy of 45.07%, mitigating over-biasing toward either head or tail classes and achieving a relatively balanced state.\n\nThese ablation studies will be included in the main text to enhance the completeness of our experimental analysis."}]

Author rebuttal:

Meta-review: This paper addresses the challenging and underexplored problem of learning from datasets that exhibit both long-tailed class distributions and label noise—a common scenario in real-world data. The authors identify a critical "deadlock" dilemma: noisy label learning (NLL) methods rely on unbiased model predictions to correct labels, while long-tail learning (LTL) methods (e.g., logit adjustment) depend on the true class distribution to mitigate bias, creating a circular dependency. To break this deadlock, they propose Unlocker, a bilevel optimization framework. The inner loop integrates NLL with LTL to enable fair noisy label selection and correction, while the outer loop adaptively optimizes the logit adjustment strength parameter τ to balance model predictions. Theoretically, the authors prove the convergence of their bilevel formulation by transforming it into an equivalent problem with a closed-form solution. Empirically, Unlocker demonstrates strong performance across synthetic and real-world benchmarks, outperforming naive combinations of NLL and LTL methods.

Unlocker tackles a critical problem with a novel, theoretically-grounded framework. The rebuttal strengthened the paper significantly, addressing all major concerns raised by reviewers. The contributions are substantial and merit acceptance as a poster. The work is not nominated for oral/spotlight due to its incremental architecture integration rather than transformative novelty, but it represents a solid advancement in the field.
Decision: Accept (poster)