Multi-Instance Partial-Label Learning with Margin Adjustment
Authors: Wei Tang
Yin-Fang Yang
Zhaofei Wang
Weijia Zhang
Min-Ling Zhang
Venue: NeurIPS 2024 poster (2024)
Keywords: Machine Learning
Multi-Instance Partial-Label Learning
Multi-Instance Learning
Partial-Label Learning
Primary area: other
TL;DR: We propose a MIPL algorithm with adjusted margins to improve generalization performance.

Abstract: Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution
loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.

Multi-Instance Partial-Label Learning with Margin Adjustment
Wei Tangl.?, 1.2, Yin-Fang Yang!.2, Zhaofei Wang 12, Weijia Zhang Min-Ling Zhang
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2 Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China
School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia
tangw@seu. edu. cn, yangyf228gmail com, wangzf @seu. edu. cn, weijia. zhang@newcsstle edu. au, zhangml@seu. edu, cn
Abstract
Multi-instance partial-label learning (MIPL.) is an emerging learning framework where each training sample is represented as multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instanee ing with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce margin aware attention mechanism to dy- namically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior perfor- mance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.
Furtial-Labe learn-
Introduction
Weakly supervised learning a powerful strategy for constructing predictive models with limited su- pervision. Based on the quality and quantity of supervision, Zhou [1] systematically categorizes weak supervision into three types: inexact, inaccurate, and incomplete supervision. Inexact supervision indicates coarse alignment between instances and labels, which is common and challenging issue in real-world tasks. Multi-instance learning (MIL) (2-3] and partial-labe learning (PLL) [2-15] are two predominant weekly supervised learning frameworks for learning from samples with inexact supervision in the instance space and the label space, respectively.
Recently, multi- cultti-instane partial- label learning (MIPL) [6] has been introduced to handle dual inexact supervision, where inexact supervision exists both the instance space and label space. Therefore, MIPL can be seen as a generalized framework of MIL and PLL. In MIPL, training sample represented as a multi-instance bag associated with a candidate label set. The candidate label set comprises one true label and the remaining are false positives. The multi-instance bag contains least one instance corresponding to the true label and does not contain any instance associated with the false positives. Additionally, positive instances refer to the instances that belong to the true label, while negative instances represent the remaining instances in the bag that are not
Corresponding author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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attention score
probability
probability
index
epoch (c) ELIMIPL
epoch (e) MIPLMA
(a) ELIMIPL
attention score
probability
probability
index
epoch (d) ELIMIPL
epach
(b) MIPLMA
(f) MIPLMA
Figure I: Margin violations in the instance space and the label space. (a) and (b) depict the attention scores of ELIMIPL and MIPLMA for the same test bag in the FMNIST-MIL dataset. Orange and blue colors indicate attention scores assigned to positive and negative instances, respectively. (c)-(f) show the highest predicted probabilities for candidate labels (green) and non-candidate labels (blue) by ELIMIPL or MIPLMA in the CRC-MIPL dataset. (c) and (e) correspond to the same training bag, while (d) and (f) refer to another training bag.
associated with any label in the label space. During training, the identities of the positive instances and the true label are inaccessible.
Dual inexact supervision widely exists in many tasks. In the classification of histopathological images, an image is frequently partitioned into a multi-instance bag due its high resolution [17-19 6 and employing domain experts for providing ground truth labels are costly. As a result, the utilization of crowd-sourced candidate label sets proves to be a valuable strategy in substantially mitigating labeling expenses [20]. To address colorectal cancer classification under dual inexact supervision, Tang et [21] have introduced the MIPL algorithm named DEMIPL. This approach employs an attention mechanism to aggregate all instances within a bag into bag-level feature representation and a disambiguation strategy to identify the true label. Following DEMIPL, ELIMIPL algorithm has been proposed to exploit the label information from both candidate and non-candidate label sets [22] Additionally, the early MIPL algorithm MIPLGP predicts bag-level label by aggregating all instance-level labels within the bag without utilizing attention mechanisms [16].
However, existing MIPL algorithms fail to consider the dynamics of the margin between attention scores of positive and negative instances, as well as the margin between the candidate and the non- candidate label sets. These oversights could lead two major issues. First, the attention scores for positive and negative instances can be quite similar, and in some cases, negative instances may even receive higher attention scores than positive ones, as illustrated in Figure a Second, the classifier may even assign higher predicted probabilities to non-candidate labels than to candidate labels. Figure Me) illustrates a scenario where ELIMIPL assigns predicted probabilities the candidate labels that are only marginally higher than the non-candidate ones. Furthermore, ELIMIPI may even output lower predicted probabilities for candidate labels than non-candidate ones, as depicted in Figure d(... Such erroneous predictions may have serious consequences in applications. For example, in medical image classification, misclassifying images of severe conditions as mild or disease-free may cause patients to miss the opportunity for timely treatment. In this paper, we term this phenomenon as margin violations, where the attention scores of negative instances surpass those of positives, or the predicted probabilities for non-candidate labels exceed those for candidate ones. Margin violations occur in both the instance and label spaces, adversely affecting the model's generalization.
To overcome margin violations, we propose a novel Partial- Label learning with Margin Adjustment. Specifically, to mitigate margin violations in the instance space, we introduce margin-awart attention mechanism to consolidate each multi-instance bag into a unified feature representation, incorporating dynamic margin adjustments for attention scores. To address margin violations in the label space, we propose margin distribution loss that adjusts the margin distribution between the model's 's highest predicted probability for candidate labels and its highest predicted probability for non-candidate labels. In Figure a), MIPLMA allocates
MIPL algorithm named MIPLMA,
i.e.,

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attention scores A
attention scores A
margin
adjustment
bag-level feature 2
instance-level features H;
margin aware attention mechanism
margin adjustment
L
Lm
2 6 S.
1 4
1
probabilities P:
classifier
Figure 2: The MIPLMA framework processes input comprising the multi-instane bag X; {a 2,,, ... 2g,9} j and the candidate label set S; = {2, 3, 5, 7}. where La and Lm represent the dynamic disambiguation loss and the margin distribution loss, respectively.
higher attention scores to positive instances and enlarges the gap between the attention scores of positive and negative instances. As illustrated in Figure and (f). MIPLMA significantly enhances classifier's highest predicted probability on candidate labels while concurrently reducing the model's is highest predicted probability non-candidate labels. Consequently, our margin adjustment strategy effectively reduces supervision inexactness in both the instance space and the label space.
Our contributions can be summarized as follows: First, we identify the phenomenon of margin violations and adjust the margins in both the instance and label spaces alleviate this issue. Second, our proposed MIPLMA outperforms state-of-the-an methods significantly. Third, the introduced margin-aware attention mechanism enhances the performance of MIL. algorithms, while the margin distribution loss improves the generalization ability of PLL algorithms.
2 The Proposed Approach
Formally, we define a MIPL training dataset as (1 m), comprising m multi-instance bags and their corresponding candidate label sets. Specifically, each candidate label set S; includes one true label, and the remaining are false positives. We denote the instance space as X = Rd, and the label space y 21, kj. covering & class labels. The i-th bag X; label set S, and the candidatdde label set S; are proper subsets of the label space y and adhere to the conditions |S, + SS = | k, where represents the cardinality of a set.
x; = ( 22,2, ... Wiin, consists of nı; instances the d'dimeensional space. Both the candidate
The overall framework of MIPLMA depicted in Figure 2 Initially, employ feature extractor vb to learn instance-level feature representations H, within the multi-instance bag X; Subsequently, we propose margin-aware attention mechanism with adjustable margins of attention scores to fuse H, into a unified feature representation z; Lastly, we utilize a classifier to predict the probabilities D, of the multi-instance bag. To identify the true label from the candidate label set, we introduce the dynamic disambiguation loss Eu and the margin distribution loss Lm.
2.1 Margin Adjustment in the Instance Space
For given multi-instaned bag X; { ...2.... ... Lin. } ddx.. comprising n, instances, utilize a feature extractor i to learn instance- feature representations, which defined as follows:
H, = ( (h.,1. h,.2.".... h.m.).
(1)
Here, H, E RIxx, represents the instance-level feature representation of the multi-instance bag X,, and h denotes the feature representation of the j-th instance in the multi-instance bag X;-
The subsequent step involves computing attention scores for each instance. MIPL, attention scores of all instances are closely distributed during the early stages of training. However, as
3

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training progresses, attention scores for positive instances gradually become higher than those for negative instances (2T] Due to dual inexact supervision, the attention mechanism struggles to differentiate between positive and negative instances during the initial training phases and calculate their corresponding attention scores. As training continues, the attention mechanism gradually assigns more distinct attention scores to positive and negative instances.
Motivated by this observation, we introduce a margin-aware attention mechanism that dynamically adjusts the margin of attention scores to achieve a closer alignment with the model's training process. The computation of attention scores given by:
A = softmax (WT (tanh [ H.) O sigm (W:] H.)) / /T()))
(2)
where W W and W22 are learnable parameters. tanh(-) and sigm(-) are the hyperbolic tangent and sigmoid functions, respectively. The operator @ denotes element-wise multiplication, and (() denotes the temperature parameter of the margin-aware attention mechanism. Specifically, in the early training stages, a larger temperature parameter is employed to smooth the distribution of attention scores, preventing the attention mechanism from assigning high scores to instances that are not unequivocally identified as positive or negative, In the later training stages, a smaller temperature parameter is used to sharpen the distribution of attention scores, thereby widening the gap between attention scores for positive and negative instances. Consequently, throughout the training process, the temperature parameter at the t-th epoch is dynamically represented follows:
(1) = max Tm, 7 (f-1) 1) 0.95),
(3)
where T. and 7(1-1) represent the minimum temperature and the temperature the 1) epoch, respectively. Therefore, Eq. (3) describes an annealing process for the temperature parameter -())
For multi-instance bags with varying numbers of positive instances, the distribution of attention scores exhibits variations. Consequently, different multi-instance bags require varying temperature parameters. To address this issue, we introduce the following normalization operations for the attention scores:
A: A
A
(4)
TH 1 1 a.)2/(nn 1)
where 1
mi Ej=1 0t,j is the mean value of the attention score A; and A, = [ū., C.. ...] R. I Subsequent to obtaining normalized attention scores, we aggregate the instance-level feature representations to compose the bag-level feature representation z; E R in the following manner:
H.A
(5)
We now discuss the theoretical properties of the proposed margin-aware aware attention mechanism. Based on the definitions of the permutation and permutation invariance (Appendix AA), the margin-aware attention mechanism can be seen as the operator A. Then, we have the following theorem:
Theorem 1.
The margin-aware attention mechanism is permutation invariant.
Theorem demonstrates that the margin- aware attention mechanism remains unaffected by the order of instances within multi-instance bags. This property is crucial for algorithms that handle set inputs The proof provided in Appendix
2.2 Margin Adjustment in the Label Space
With the aggregated bag-level feature representation, utilize classifier that synergizes dynamic disambiguation loss and margin distribution loss to identify the true label.
The aim of our dynamic disambiguation loss is to progressively identify the true labels by calculating the classification loss, as illustrated below:
(4) Pie log (pg()
La
(6)
m OES.

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where Pi,e and Pic represent the weight and predicted probability, respectively, on the e-th class at the t-th iteration. This weight represents the probability that the corresponding candidate label the true label, which is initialized as follows:
(9)
(()
(0) Piçc
[8.]
1
if S
0
otherwise,
where ||
represents the set cardinality. In the t-th epoch, update the weight as:
(0) Piçe
Pie ( p(t-1)) + (11-a())
if St otherwise,
Eves; Pie
0
where (1 = - is tuning parameter used to balance the update speed of the weight, and T the maximum number of training epochs.
The dynamic disambiguation loss adjusts the classifier's predicted probabilities for the candidate labels, without affecting the probabilities assigned to non-candidate labels. As illustrated in Figure Id), this circumstance may result the classifier assigning its highest predicted probability to non-candidate label instead of candidate label, i.e., margin violations. To mitigate potential issues in model generalization, is crucial to maintain a significant margin between the highest predicted probabilities for the candidate and non-candidate labels. Therefore, propose the margin loss to maximize the margin between the highest predicted probability on the candidate label set and on the non-candidate label set, as shown below:
ml
m Σ
(1) [1 (max Piee max P Pio) cES; BES,
(1) where max.es; Pi,c and max Piça are the highest predicted probabilities on the candidate label and the non-candidate label set, respectively. However, only considering the mean margin cannot effectively address margin violations, thus affecting the performance. Some recent studies have shown that the model performance can be enhanced by maximizing the margin mean and minimizing the margin variance simultaneously (24]26) 26). Therefore,
employ two statistics of the margins ie., the margin mean and the margin variance, to adjust the margin distribution. Specifically, we can maximize the margin mean and minimize the margin variance between the highest predicted probability on the candidate label set and on the non-candidate label set simultaneously by minimizing the following margin distribution loss:
Lm M(6i. ... ......... .mm
(10)
VVlo 02.... ... mm
where d; = (1 (max.es, Pie max P( refers to the margin loss of the i-th multi-instance bag. MM.}} and V are the mean and the variance of the margin loss, respectively.
During training, the full loss is represented as the weighted sum of the dynamic disambiguation loss and the margin distribution loss, as expressed below:
L La + ACm:
(11)
where À represents hyperparameter.
3 Experiments
3.1
Experimental Configurations
3.1.1 Datasets
Following the experimental setup of DEMIPL 21,, we utilize four MIPL benchmark datasets and one real-world dataset. The four benchmark datasets encompass MNIST-MIPL, FMNIST-MIPL, Birdsong- MIPI, and SIVAL-MIPL, spanning diverse application domains such as image analysis and biology Additionally, the real-world CRC-MIPL dataset is annotated by crowdsourced workers for
27 30
5

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Table 1: Characteristics of the benchmark and real-world MIPL datasets.
Dataset
#bag
#ins
max. tins
min. Hins
avg.
#ins
fdim
#class
avg. #CLs
MNIST ANIST-MIPL. I-MPL.
500
20664
48
35
41.33
784
5
2, 3,
FMNIST-MIPL
500
20810
48
36
41.62
784
5
2,
Birdsong-MIPL
1300
48425
76
25
37.25
38
13
2,
SIVAL-MIPL
1500
47414
31.61
2.
C-Row
7000
56000
8
8
9
7
2.08
C-SBN
7000
63000
9
15
7
2.08
C-KMeans
7000
30178
4.311
7
2.08
C-SIFT
7000
175000
25
2.08
C-R34-16
7000
112000
16
16
16
1000
7
2.08
C-R34-25
7000
175000
25
25
25
1000
7
2.08
colorectal cancer classification. The previous works 21 22 employ four distinct types of multi- instance features and consists of four sub-datasets: CRC-MIPL-Ren (C-Row), CRC-MIPLSRN (C-SBN), URC-MIPI-MMannne (C-KMeans), and CRC-MIPLSIIT (C-SIFT). These multi-instance features are generated via four image bag generators [11. i.e., Row, single blob with neighbors (SBN), k-means segmentation (KMeansSeg), and scale-invariant feature transform (SIFT), respectively. Besides these multi-instance features, we are the first employ the ResNet [32 learn the multi-instance features of CRC-MILL dataset. Specifically, we partition each image into N non-overlapping patches, treating each patch as an instance. Subsequently, the ResNet-34 is employed to acquire feature representations for each patch, resulting in feature representations of dimension 1000 for each patch. In our experiments, the N is 16 and 25, and the resulting datasets are CRC-MIPL-ReNNesS (C-R34-16) and CRC-MIPE BRC-MMPL-ReNNN:3--2 (C-R34-25).
The characteristies of the dataset detailed in Table It provides the number of multi-instnn iulti-insttace bags and total instances, denoted as thag and #ins, respectively. Furthermore, we employ max. #ins, min. #ins, and avg. #ins express the maximum, minimum, and average instance count within all bags. The dimensionality of each instance-level feature representation is indicated by #dim. #class and avg. #CLs denote the length of the label space and the average length of candidate label sets, respectively. For a comprehensive performance assessment, we vary the number of false positive labels on the benchmark datasets, represented as ([S, = 1).
3.1.2 Comparative Algorithms
We conduct comprehensive comparison of MIPLMA with a wide variety of baselines, covering MIPL, PLL, and MIL algorithms. For MIPL algorithms, we compare with MIPLGP (16). DEMIPL [21] and ELIMIPL [22] In our evaluation, we incorporate seven PLL algorithms, featuring five
Rc Lws 35 CAVL 11. and POP [36], one feature-aware disambiguation algorithm, PL-AGGD
deep-learning-batt approaches: PRODEN [33
37 and two margin-based based algorithms, M3PL
38 and PL-SVM [39]. Furthermore, our comparison encompasses seven MIL algorithms. Three of the MIL algorithms are Gaussian processes-based: VWSGP 40), VGPMIL 41 and LM- VMMMIL
41 Additionally, a variational autoencoder-baset algorithm, MIVAE 42 and three attention-based algorithms: ATTEN 4 ATTEN-GATE 44, and LOSS-ATTEN
43 are included.
The deep-learning-bete based PLL algorithms [33 BS | can be equipped with either the linear model or multi-layer perceptrons (MLP) as backbone networks. Results obtained from the linear model are presented in the main body of the paper, while additional experiment results are detailed in the Appendix C Parameters for all compared baselines have been meticulously tuned, drawing from recommendations in the original literature or refined through our pursuit of improved performance.
3.1.3 Implementation
We implement MIPLMA using Py PyTorch 44 and conduct training with a single NVIDIA Tesla V100 GPU. Employing the stochastic gradient descent (SGD) optimizer, we set the momentum value to 0.9 with weight decay of 0.0001. To learn the instance-level features, we employ a two-layer convolutional neural network and a fully connected network for the MNIST-MILL and FMNIST-MIPL. datasets. Since the features of the Birdsong-MiPr, and SIVAL-MIPL datasets are preprocessed, we only employ a fully connected network to learn the feature representations. For the CRC-MIPI dataset, the feature extractor is one of the four image bag generators ResNet-34, followed by fully

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Table The classification accuracies (st) of MIPLMA and comparative algorithms on the
benchmark datasets with the varying numbers of false positive labels (r {1,2,3)
Algorithm
MNIST-MIPI
FMNIST-MIPL
Binkong-MMR MIPI.
SIVAT-MIPL
985.1.010
.91:1.006
.776.1.020
.7031.002
MIPLMA
.979.10014
-867:1.02
.6621.015
.68.1.031
.749:1.103 3
6644 055
.7464.013
.67:--024
.992.1.007
_90311018
.7711.0088
675.1.022
ELIMIPL
.9874.010
.84:2.026
.745-:0015
.61:::025
.748.1.
.702.1.05
.7174.017
600.1.029
976:1.008
881:E.211
.744:1.016
635:5.041
DEMIPL
2
.943 113.1.023
.82311.22
77011.02
554.1.051
.709...08
657-4.025
.6961.024
503-1-018
049-4.016
847:.033
7766 026
669
MIPLGP
2
817=.003
-791#.02) 027
.6724.015
.633t 026
56991.
Mcan
MaxMin
Mean
MaxMin
Mean
MaxMin
Mean
MaxMin
660 023
508 024
.69.1 042
424 005
.29612 014
-187
.1992.014 014
.36:2.019
PRODEN
481 036
007=
.73 026
.377 040
.222 019
012
184 014
.277 024
3
283 028
345 048
345 027
309
2111::13
23122012
166.1 017
.250 0118
65881.033 031
519:: 028
.753
731
3622:005
.3902.014 014
.279: 001
.306
RC
1998.1.08
4691.035
.699 028
566.1 027
33111001
1.1
.258.1.017
.288 1. .021
3
392.1: 033
380 048
401 + 063
524 034
298 009
.363=000 010
.237
267 020
A35
.265
.289
LWS
209 028
239=:008
.720 025
406 040
.10
2207=034
.2232 008
205 L. 013
218::017
.579 1. 041
.318 318.1.006 1.64
.37 005
.2162
.1941 026
2244 0023
481=
728
025
370 012
2600
251 023
CAVL
2
A12:1.039 039
389:1.027
586 1. 055
26411.037
017
.335 008
2337
.216: 001
.315
.22.1.032
.353
.265
.313
.197
0533
511::032
799 032
.09 044
383 009
388= 015
2215:.012
.161 :15
POP
2
585 045
438=0037
.725 025
.395 028
.38 011
018
.78 020
.96 020
010
671 027
527=
743 026
391 040
353 019
383= 014
355
397 028
PL-AGGD
5555 036
439=0020
-677-.028 028
.771
.184
3372= 020
.11520019
.360#.029
380 032
1213.003
0557
337.1.026
.296:.015
344
.2861 018
.328.1 023
connected network. The initial learning rate is chosen from the set f0.01, 0.05) and coupled with a cosine annealing technique. We set the number of epochs to 100 for benchmark datasets and 200 for the CRC-MIPL dataset. The weight of the margin distribution loss is chosen from the set {0.01, 0.05, 0.5, 3,3.5) for all datasets. For the annealing process of the temperature parameter, the initial temperature parameter -( = 5. Additionally, Tm 0.1 and Tm 0.5 are used for benchmark datasets and the CRC-MIPL dataset, respectively. The dataset partitioning method aligns with that of DEMIPL [21] and ELIMIPL [22]. We execute ten random train/test splits, maintaining ratio 3. Mean accuracies and standard deviations from these ten runs are reported. The code of MIPLMA can be found at https:://// gi thub. com/ http://aagsse/MMMMMM
3.2 Comparison with MIPL and PLL Algorithms
Since PLL algoritmms can not directly handle the multi-instance bags, we utilize two data degradation strategies: the Mean strategy and the MaxMin strategy [16]. The former computes the average feature values across all instances within a bag for producing bag-level feature representation. The latter identifies both the maximum and minimum feature values for each dimension among instances within a multi-instance bag and concatenates these values to form bag-level feature representation.
3.2.1 Results on the Benchmark Datasets
Table 2 provides a comprehensive comparison of the results achieved by MIPLMA, three MIPL algorithms (ELIMIPL (22). DEMIPL [21]. and MIPL Gp [16)), five deep-learning based PLL al- gorithms (PRODEN 33 Rc 34. LWs [35) CAVL [. and POP (36) with lincar model, and
the feature-aware disambiguation PLL algorithm (PL-AGGD 37... The evaluation is conducted on benchmark datasets with varying numbers of false positive labels (r E (1,2,3)).
Notably, MIPLMA consistently exhibits higher average accuracy than the three MIPL algorithms in 33 out 36 cases. For the methods based on the embedding space paradigm, MIPLMA demonstrates superior performance compared to ELIMIPI. and DEMIPL. in 21 out of 24 cases. Compared to MIPI.G GP that follows the instance space paradigm, MIPI.MA achieves higher average accuracies than it in all cases. Specifically, on the SIVAL-MIPL dataset, the average accuracies of MIPLGP consistently
7

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Table The classification accuracies (mean of MIPLMA and comparative algorithms on the real-world datasets. means unavailable due to computational limitations.
Algorithm
C.Row
C-SBN
C-KMeans
C-SIFT
MIPLMA
14411.010
.526 $209
.557 1000
.553:.0009
ELIMIPI.
453 .008
-09-1.007
.54611012
.5001.010 .532:2013
DEMIPL
408 010
486 014
.51 #.12
MIPLGP
4324 005
335=
329:
Mean
MaxMin
Mean
MaxMia
Mean
MaxMin
Mean
MaxMin
PRODEN
365 009
401: 007
.9222.08
4474.011
2332 018
027
334
291
214:.0011
.2774.012
24224.022
338:10010
226:.009
208::.007
209++0007
246:2.008
LWS
29111.010
299.1.008
.3001.006
382.1.009
008
247.1.005
.20-1.007
230.1.007
CAVI.
.12.4.045
.3881. 054
.664
5034:025
.266.1 062
.3114.038
423
274
POP
383+.001
.33- 015
439+-009
4384.010
385 016
.79 016
326++.013
278:2004
PL.-AGGD
.12.1.008
.60.1.008
4.05
524.1.008
358.1.008 008
434.1009
.363 1612.012
.285
exceed those of DEMIPL. However, the average accuracies of MIPLMA surpass all algorithms on the SIVAL-MIPL dataset, thus highlighting the effectiveness of MIPLMA.
Additionally, MIPLMA significantly outperforms PLL algorithms in all cases. For relatively sim- ple datasets such as MNIST-MIPL and FMNIST-MIPL, the PLL algorithms demonstrate satisfactory performance. However, with the increasing complexity of datasets, as observed in Birdsong-MIPL and SIVAL-MIPL, the effectiveness of the PLL algorithms noticeably diminished. On MNIST-MIPL and FMNIST-MIPL, the Mean strategy generally outperforms the MaxMin strategy. Conversely, on Birdsong-Mune MIPL and SIVAL-MILL the Max.Mn strategy tends to yield superior results in most cases compared to the Mean strategy. Hence, the two data degradation strategies do not uniformly outper- form each other but have their respective advantages. The selection the degradation strategy is dependent on the characteristics of the dataset. For simpler datasets, straightforward Mean strategy may suffice, while for more complex datasets, a sophisticated MaxMin strategy may be preferable.
3.2.2 Results on the World Datasets
Table 3 presents presents a detailed comparison of results on the CRC-MIPL dataset. Our method, MIPLMA, demonstrates superior performance in all 11 cases when compared to ELIMIPL (22). DEMIPL [2I]. and MIPLGP [16]. In terms of the PLL algorithm, MIPLMA also achieves superior accuracies in all cases. While the PLL algorithms yield satisfactory results on relatively simple datasets like their performances noticeably deteriorate when handling more
CRC-MIPL and CRC-MIPL
complex datasets such as CRC-MIPL KMeans and CRC-MIPL-SIFT.
Moreover, both MIPLMA and ELIMIPL demonstrate significantly better performance on the CRC- MIPL-KMeans and CRC-MIPL-SIFT datasets compared to the CRC-MIPL-Row and CRC-MIPL-SBN datasets. However, this trend is reversed for MIPLGP and the PLL. algorithms. We attribute this discrepancy to the incapacity of these algorithms effectively model complex features. Particularly, the limitations of the PLL algorithms become more apparent when dealing with complex MIPL data. Therefore, there is an urgent need devise more effective MIPL algorithms.
3.2.3 Results of the CRC-MIPI. Dataset with Deep Features
Tang et al. (2] have introduced the CRC-MIPL dataset, extracting multi-instance features using four hand crafted image bag generators [31. Both DEMIPL (21) and ELIMIPL [22] were evaluated using these multi-instance features in the literature. In this study, we investigate CRC-MIPL with neural network generated features and employ ResNet to learn deep multi-instance features from the CRC-MIPL dataset. The resulting datasets are referred to as C-R34-16 and C-R34-25.
Table 4 illustrates the classification accuracies of MI- PLMA, ELIMIPL, and DEMIPL on the CRC-MIPL dataset with deep multi-instance features. From the experimental results, two key observations emerge: (a) DosNeet334-bssee features outperform those generated by image bag gener- ators in terms of classification performance. (b) When learning multi-instance features with ResNet-34, dividing an image into 25 instances results in more discriminative feature representation compared to using 16 instances.
Table 4: The classification accuracies tstd on the CRC-MIPL dataset with deep multi-instance features.
Algorithm
C-R34-16
C-R34-25
MIPLMA
.631-1008 .685.1.011
ELIMIPL
.628.1.009
.6634.009
DEMIPL
.625t.008
.6502.010
8

---

Table 5: The classification accuracies (sta of MAAM, ATTEN, and ATTEN-GATE on the MIL datasets with bag-level true labels.
Algorithm
MNIST-MIPL (MI))
FMNIST-MIPL(ML)
MAAM
.9911.006
.930-.021
ATTEN
.962+.010
.859+-032
ATTEN-GATE
.911-0117
.847+-037
Table The classification accuracies (te) PRODEN-MA and PRODEN on the Kuzushiji- MNIST dataset with varying flipping probability q.
Algorithm q= 0.1
q= 0.3
q= 0.5
= 0.7
q= 0.9
PRODEN-MA
.922-.001
992-110002
.1441.002
.892-1.01 .866..010
88161.008 .772+.017
PRODEN 900610002
9001
.884.0005
In summary, feature representations learned by the deep feature extractor ResNet-34 exhibit higher discriminative capacity compared to those generated by image bag generators. Our model consistently achieves the highest classification accuracy among these three MIPL algorithms, especially on the C- R34-25 dataset. These observations suggest that our model not only achieves the highest classification accuracy on traditional features but also handles deep features better.
3.3 Effectiveness of the Margin Adjustment
To assess the effectiveness of margin adjustment, we introduce three variants of MIPLMA. MIPL- MAINS denotes the margin adjustment of attention scores exclusively, with in Eq. (1T) set
MIPI-MALAB signifies the margin adjustment of predicted probabilities only, where ((0) in Eq. 2
assigned for 2 = 1,2, ................................ T. MIPL-WWME. indicates no margin adjustments of attention scores
or predicted probabilities, with X = 0 and (()) =
1 for t = ,
Figure 3 demonstrates that MIPLMA consis- tently outperforms three variants, proving that margin adjustment in the instance and label spaces can significantly enhance model perfor- mance. Additionally, adjusting the margin in the label space yields better results than in the instance space, validating the effectiveness of our proposed margin distribution loss. From another perspective, adjusting the margin of predicted probabilities directly impacts classi- fication accuracies, whereas adjusting the margin of attention scores affects the beg-level feature representations, thereby influencing classification accuracies. Consequently, MIPL-MALAB demon- strates superior performance than MIPL-MAINS. By simultaneously adjusting the margins in both the instance and label spaces, i.e., MIPLMA, optimal results can be achieved. This underscores the effectiveness of our margin adjustment strategy in dealing with the inexact supervision of MIPL.
MiPL-MALAB Min. HOM.
accuracy
0.8
MAINS
0.6
0.2
T=1
T=2 2
Figure 3: The classification accuracies (mean and std) of MIPLMA with the three variants on the SIVAL-MIPL dataset (r E {1, 3)).
3.4 Margin Adjustment for MIL and PLL Algorithms
In MIPLMA, the margin adjustments for attention scores and predicted probabilities reduce the supervision inexactness in the instance space and the label space, respectively. MIPL is generalized framework of MIL and PLL. Therefore, this raises a pertinent question: can margin adjustment enhance the performance of MIL and PLL algorithms?
To answer this question, we propose a MII. algorithm named MAAM that is simplified variant of MIPLMA. We compare MAAM with two classical MIL methods incorporating attention mechanisms, namely ATTEN [4] and ATTEN-GAR -ATE [4] on the MNIST-MILL (MIL) and FMNIST-MIPL (MIL) datasets. During training, we use only the features of multi-instance bags and their corresponding bag-level true labels. The parameters of MAAM for the two datasets are as follows: learning rates of 0.005 for MNIST-MIPL. and 0.01 for FMNIST-MIPL. Additionally, (0) = and Fm = 0.1 for both datasets. Table presents preseets the classification accuracies over ten runs, indicating that MAAM outperforms both ATTEN and ATTEN-GATE, particularly on the FMNIST-MIPL. dataset. These results demonstrate the effectiveness of MAAM, confirming its superior performance.

---

Moreover, we equip the PLL algorithm PRODEN with the margin distribution loss, resulting in the variant PRODEN-ME MA. Table presents the classification accuracies of PRODEN -MA and PRODEN on the KuzushijiMMNST" dataset with varying flipping probability É {0.1, 0.3, 0.5, 0.7, 0.9). The only difference between PRODEN-MA MA and PRODEN lies in that PRODEN M includes the margin distribution loss with the weight of 1. We employ MLP as the backbone network for both PRODEN- MA and PRODEN, and keep all other parameters consistent. Experimental results indicate that PRODEN-MA outperforms PRODEN across all scenarios. Notably, under higher disambiguation difficulty, i... q = 0.9, the superiority PRODEN-MA is more pronounced.
In summary, adjusting the margins of attention scores improves classification performance for MIL algorithms. Similarly, margin adjustment in the label space enhances the performance of PLL algorithms, particularly in challenging disambiguation scenarios.
4 Conclusion
This paper investigates the margin adjustments in both the instance space and the label space for MIPL. We propose MIPLMA, which incorporates margin- attention mechanism and a margin distribution loss to adjust the margins for attention scores and predicted probabilities, respectively. Experimental results the benchmark and real-world datasets illustrate the superiority of our proposed MIPLMA algorithm over a diverse set of baselines, encompassing MIPL, PLL., and MIL algorithms. Specifically, MIPLMA achieves superior performances compared baselines in 96.4% of cases. These results underscore the effectiveness and significance of our margin adjustment strategy.
However, MIPLMA has several limitations. First, similar to other attention-based MIL and MIPL methods, it cannot process multiple multi-instance bags simultaneously. Second, MIPLMA demon- strates slight overfitting problem on the relatively simple MNIST-MILL dataset. Third, MIPLMA is not suitable for instance-level classification tasks. In the future, we will delve into designing MIPL algorithms capable of instance-level classification and parallel algorithms that can handle multiple multi-instance bags concurrently.
Acknowledgements
The authors wish thank the anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Science Foundation of China (62225602) and the Big Data Computing Center of Southeast University.
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13

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Multi-Instance Partial-Label Learning with Margin Adjustment (Appendix)
A Proof of Theorem
For notation simplicity, we denote the instance-level features H; as H for the rest of this section. First, we definite the permutation and permutation invariant as follows. Definition 1. (Permutation). We characterize Q Rixa + R as a permutation operation, denoted by ((H) HQ, where Q TR a permulation mairix employed to rearrange the instance order within H. Specifically, is an orthogonal matrix, ensuring Q QQ=1.
Definition 2. (Permutation Invariant). An operator A Rix= + R is permutation invariant concerning the instance order in H ((A(H)) ((H) is satisfied [23).
Theorem 1. The margin-aware attention mechanism is permutation invariant.
Proof. Let H = (h1: h
hn.) denote the instance-level feature representations of the multi-
instance bag X. We rewrite the computation of attention scores Eq. ( as follows:
A HH)
softmax (W (tanh(W]] H) @ sigm W H))/T)
(A1)
softmax ((H)/))
The normalization operations of attention scores and the aggregated bag-level feature representation are shown below:
A' (A) (AS(H))
(A2)
A(H) = HA' = HV)
(A3)
where (-) represents the function of the normalization operations and A' signifies the normalized attention scores.
Given the permutation matrix Q satisfying Q QQQ= = I. we obtain the permuted feature Q(H) = HQ.
Firstly, the attention scores are computed follows:
AS(HQ) (oftmax({(HQ)).
softmax [
(tanh( [ HQ) sigm W HQ))/T)
softmax (W])
(tanh(W) W H)Q sigm W, H)Q)/T)
softmax W
(tanh(W (W] H sigm (, H))//))
(A4)
softmax softmax({(H)O))) ({(H)Q/T)
softmax({(H)))
AS.
As depicted in Equation (4), in the normalization operations, neither the denominator nor the mean value of attention scores is affected by the permutation matrix Q. Therefore, we can derive the following equation:
V (AS(HQ)) ((A((H))) (AS(H)QQ
(A5)
Subsequently, the resultant representation of the aggregated bag-level features is expressed as:
A(HQ) (QS(AS(HQ))
(AS(()))))
HQQ (H))
(A6)
-HW(AS(H))
= -HH).
Therefore, the margin-aware attention mechanism is permutation invariant.
14

---

Algorithm Y, MIPLMA (D, T. X.)
Inputs:
D: the MIPL training set {(X, ;, S;) isis m). where X; ( 22,2," 1 -iin Bis E EX,
X= IRd S. CV. = (1,2,
A: the weight for the maximum-maan -argin disambiguation loss
T: the maximum number of training epochs
X, the unseen
bag with n. instances
Outputs:
Y. the predicted label for X
Process:
1: Initialize uniform the weights on candidate label set Piçc as stated by Eq.
2: for I. 1 to 7 do
3: Shuffle training set 'D into B mini-batches
4: for b = 1 to B do
5: Extract the instance-level features according Eq. 4
6:
Calculate the attention scores with the temperature (()) stated by Eqs. and
7:
Normalize the attention scores as stated by Eq. (4)
8:
Aggregate the instance-level feature representations into the bag-level feature representation according to Eq. (5)
9:
Classify the multi-instance bag with predicted probabilities.
10:
Update the weights p, according to Eq. (8
11: end for
12: Calculate the full loss L according to Eq.
13: Update the model & by the optimizer
14: end for
15: Extract the instance-level features of X. according to Eq. TT
16: Calculate the attention scores and map the instance-level features into single vector representa-
tion 2, according to Eqs. (2). (3) (4), and (5)
17: Return Y. = arg max px,c eEy
B Pseudo-Code of MIPLMA
Algorithm|| describes the complete procedure of MIPLMA. First, the algorithm uniformly initializes the weights the candidate label set (Step 1). In each epoch, the training set is divided into multiple mini-batches (Step 3). Then, instance-level feature representations are extracted for each mini-batch and aggregated into bag-level feature representation (Steps 5-8). The subsequent step involves classify the multi-instance bag and updating the weights on the candidate label set (Steps 9-10). Last, the full loss is calculated, and the model is updated (Steps 12-13). For an unseen multi-instance bag, instance- level feature representations are learned and aggregated into bag-level feature representation via the feature extractor and the margin- -wware attention mechanism, respectively (Steps 15-16). The predicted label is the category corresponding to the highest prediction probability (Step 17).
C Additional Experiment Results
C.1 Comparison with Margin-based PLL. Algorithms
In the field of PLL, there exist several disambiguation algorithms that rely on the maximum margin criteria, with PL-SVM and M3PL emerging notable algorithms. PL-SVM achieves disambiguation by maximizing the margin mean between the highest prediction probability on the candidate label set and that on the non-candidate label set. In contrast, M3PL maximizes the margin mean between the highest and second-higher prediction probabilities of the classifier for disambiguation. Consequently, we conduct a comparative analysis of classification performance using the MIPLMA algorithm against PL.SVVM and M3PL. Figures A1 and present the average accuracy and standard deviation of the three algorithms over ten runs on benchmark and real-world datasets, with Mean and Max representing the corresponding data degradation strategies.
15

---

1.0
1.0
accuracy
0.8
accuracy
0.8
accuracy
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
r=
3
r r=
==
T= 1 3
MNIST-MIPL.
FMNIST-MIPL
Birdsong-Min
0.8
0.6
MIPLMA
accuracy
accuracy
0.6
0.4
M3PL (Mean)
0.4
M3PL (MaxMin)
0.2
PL-SVM (Mean)
PL-SVM (MaxMin)
2
T=3
3
C-SRN C-KMans CRC-MIPL
SIVAL-MIPL
Figure Al: The classification accuracies (mean and std) of MIPLMA, M3PL, and PL-SVM.
Table A1: The classification accuracies (tean=sttd) of MIPLMA and comparative MIL algorithms on the benchmark datasets with one false positive candidate label (r 1).
Algorithm
MNIST-MIPL
EMNIST-MIPL
Birdsong-MirL
SIVAL-MIPL
MIPLMA
985+.010
.915+.016
.776+.020
.703+.026
VWSGP
.13+.043
.155.005
.271+.045
.050+.013
VGPMIL
4481.052
4811.030
.07211038
.042:1.006
LM- LM-VGPMIL
.83110500
46511.024
.0791.055
.04611008
MIVAE
.6921.221
60311.212
.14211191
.09811.13
ATTEN
499+.010
408+.069
.1344.020
.1004.021
ATTEN-GAN
504+.078
357
.161
.10+t0022
LOSS-ATTER
8424.051
.7774.048
.537=.025
320+.008
MIPLMA consistently outperforms PL-SVM and M3PL algorithms on both benchmark and CRC-MIPL datasets. This superiority can be attributed to two primary factors. First, within MIPLMA, the margin distribution Joss simultaneously maximizes the mean margin and minimizes the variance of predicted probabilities. In contrast, PL-SVM and M3PL only maximize the mean margin of predicted probabilities. Therefore. our proposed margin distribution loss provides a more refined approach to optimizing the margins of predicted probabilities. Second, unlike the data degradation strategies employed by PLL algorithms for the indirect treatment of multi-instance bags, MIPLMA aggregates each multi-instance bag into single bag-level feature representation using the margin- attention mechanism. The aggregated feature representations in MIPLMA are more discriminative than those obtained using data degradation strategies. Additionally, PL-SVM and M3PL employ iterative optimization strategies, rendering them challenging to integrate with deep models.
C.2 Comparison with MIL Algorithms
Existing MIL algorithms are primarily tailored for addressing binary classification problems and not readily applicable to solving MIPL problems. To overcome this limitation, we adopt the One vs. Rest (OvR) decomposition strategy employed in MIPL.GP [16] for degradation of MIPL data. Specifically, when presented with a multi-instance bag X, linked to a candidate label set S,, we assign each label from the candidate set the bag, and thus generate [S,] multi-instance bags where each bag is associated with a singular bag-level label. For each class (c E {1, 21,2,- -.. k)). we train and test the e-th classifier by transforming the label to (positive) and all other labels 0 (negative). the testing phase, a multi-instancd bag yields & predictions from the classifiers. If only one prediction is positive, the corresponding class label that positive prediction considered the classification result for the bag. In cases where there is more than one positive prediction among the & predictions, we choose the class label associated with the classifier demonstrating the highest prediction confidence as the classification result for the bag. If all & predictions the class label with the lowest prediction confidence. In particular, LOSS-ATTEN multi-class MIL algorithm, climinating the need for employing the One vs. Rest decomposition strategy. We can
negative, the classification result is
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Table A2: The classification accuracies (mean=sstd) of MIPLMA and comparative PLL algorithms with MLP on the benchmark datasets (r E {1. 2.))
Algorithm
MNIST-MIPI.
FMNIST-MIPI
Bindongg MIPI.
SIVAL-MIPL
.985 1.00
915:1.016
.776.1.020
.7031.006
MIPLMA
-97911.04
.867 1828
.762 262:1.015
.66811031
.749:t.03
.64::.055
.7464.013
.627:t024
MaxMin
Mean
MaxMin
5551:0033
023
662. 033
.3884009
.014:006
010
.3032.000
.3224
PRODEN
2
372 038
338=-031
463+ 067
.154.023
287 017
016=
.244 022
2255
.02
260=
288
.255.0031
278 006
=35=:015
.242
.244:0018
660+ 031
518= 022
697 106
4216
.399 014
374=
3444.014
.304
RC
$77:40099
A62=-028
6844 029
.363
.311=:014
.39==0015
.299-.015
.268 023
362. 029
366
A14 050
294 053
.288 019
.332=
.26 013
244 014
.605 030
457
.702.1 033
.3661.033
.3441 0188
.349
.346 014
.3451.013
A31
-41
.57--
3214.001
.310
.336=
.3122.015
.144.019
3
.335 029
-774=037
A11 033
2677
.289 021
336=0016
286=.018
.288
.3391: 048
495
007
.394
.3122 014
.332=0011
22072.010
.220
CAVL
380 040
337=
A82 097
321
.855 014
-30==022
.17=
.999 014
3
266-1 021
226
288 L: 060
266-1.033
274
.282-0100
0017
.144 2011
505 H. 029
4432.032
64111.033
3191 020
341
.3762.019
.3442.014
POP
315 022
281
167.1.037
19211001
.999 020
.337
.134.1 029
.344..00 .012
258 041
026
256- 025
239
281 016
312=
.302 022
3104-022
Table A3: The classification accuracies (mean-std) of MIPLMA and comparative PLL algorithms with MLP on the real-worle world datasets.
Algorilbm
C-Row
C-SBN
C-Meeans
C-SIFT
MIPLMA
444 010
326 009
.357 2.010
.8532.009
Mean
MaxMin
Mean
MaxMia
Mcan
MaxMin
Mean
MaxMin
PRODEN
4054.012
4531_0009
5154.010
.529:1.010
5124.014
-631.011
3524,005
2943 008
RC
290+
347::0013
3944.001
432+.0008
3044.017
366::000
2484.008
204::000
36011.08
.3811.011
.4011.009
.42:1.009
A224..035
.89:1.009
CAVL
39411.014
.36611010
475.1.017
356.1.025
409. 009
48311.030
321 1.04
279.1.014
POP
.9999.011
.374:.019
.5134.020
431:1.026
5034..014
.440:.017
.3334.009
.2334.021
directly assign all candidate labels multi-instance bag X; as the label for the and obtain [S,] multi-instance bags with singular bag-level label.
bag
Table Al presents the classification accuracy of MIPLMA and seven comparative MIL algorithms. Across all scenarios, MIPLMA consistently demonstrates significantly superior performance com- pared to the seven MIL algorithms. Notably, among the compared MIL algorithms, LOSs-ATTEN consistently outperforms its counterparts, primarily due to its capability of directly addressing the multi-class MIL problem. While these algorithms yield reasonable results on the MNIST-MIPL and FMNIST-MII datasets, their performance diminishes severely datasets with more intricate features, such as Birdsong- and SIVAL- We attribute this challenge to the noise present in the labels due to data degradation, hindering the effective learning of these MII. algorithms.
C.3 Results of PLL algorithms with MLP
Among the compared PLL algorithms, PRODEN RC 34, with either linear model or MLP. Tables and 3 the main body present the results obtained using the linear model. In this section, we present the results of MIPLMA and the comparative PLL algorithms with MLP on the benchmark and CRC-MIPL. datasets in Tables A2 and A3 respectively.
LwS(35), and CAVL can be utilized
Table A2 presents the performance comparison of MIPLMA with the four PLL algorithms utilizing the MLP on benchmark datasets. MIPLMA consistently outperforms the comparative PLL algorithms in cases, demonstrating statistically significant superiority. Interestingly, when employing the MLP, the compared PLL algorithms do not consistently achieve superior outcomes compared to the linear model. This observation suggests that the linear model has already captured sufficient features for benchmark datasets, while employing the MLP may lead to overfitting.
Table A3 demonstrates that MIPLMA surpasses the comparative PLL algorithms utilizing the MLP in 37 out of 40 cases. Compared to the outcomes obtained using the linear classifiers, the results obtained using the MI.P exhibit superior performance. Notably, on the complex CRC-MIIL.KO MManss dataset, the enbancement with the MLP is more pronounced. This indicates that the linear model is
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Table A4: Win/tie/loss counts of MIPLMA against the compared algorithms.
MIPLMA against
In total
MMIL algorithms
PLL algorithms
MIPL algorithms
r= 2
28/0/0
104/0/0
9/2/1
141/2/1
104/0/0
10/1/1
114/1/1
=
104/0/0
7/4/1
111/4/1
CRC-MIPL
98/4/2
13/2/0
111/6/2
In total
28/0/0
410/4/2
39/9/3
477/13/5
inadequate for comprehensive feature learning on the CRC-MIPL dataset, while the MLP demonstrates a more comprehensive capability in feature learning.
C.4 Win/tie/loss counts of Experimental Results
To ensure result reliability, we conduct pairwise t-tests with a significance level of 0.05. Table A4 summarizes the win/tic/loss counts between MIPLMA and seven MIL, seven PLL, and three MIPL algorithms on benchmark datasets with varying false positive labels (r {1, 2))). well as the CRC-MIPL dataset. Several key insights can be drawn from our analysis: (a) MIPLMA shows statistical superiority over MIL, PLL, and MIPL in 100%, 98. 6%, and 76.5% of cases, respectively.
(b) MIPLMA achieves statistical superiority in 97.3% of cases on benchmark datasets. (c) For the CRC-MIPL dataset, MIPLMA shows statistical superiority in 91.6% of cases. Overall, MIPLMA outperforms the compared algorithms in 96.4% of cases.
D Further Analyses
D.1 Interpretability of the Attention Mechanism.
To investigate the interpretability of attention mechanisms, we compare MIPLMA with ELIMIPL and DEMIPL regarding the attention scores across three test multi-instance bags in the FMNIST-MIPL dataset (r 1). Each row in [gg depicting the attention scores produced by MIPLMA, ELIMIPL, and DEMIPL across the three multi- instance bags. It is noteworthy that since the sum of attention scores computed by DEMIPL does not equal 1, we initially normalize them to sum to before visualization.
a multi-instance bag, with the first to third columns
Figure A2 reveals several important findings. First, on the Bags 1 and 2, both our MIPLMA and ELIMIPL accurately identify positive instances. However, compared to ELIMIPL, MIPLMA assigns higher attention scores to positive instances and lower scores to negative ones, confirming the efficacy of our margin-aware attention mechanism. Second, the Bag 3 contains four positive instances, of which MIPLMA only identifies three, erroneously assigning high attention scores to several negative instances. However, the highest attention score assigned to a negative instance is still lower than the scores of these three positives. In contrast, ELIMIPL assigns significantly higher attention scores to two negative instances compared to the scores of the four positives, leading to a pronounced influence of negative instances on the aggregated bag-level features. Third, DEMIPL computes attention scores using the sigmoid function followed by normalization, while MIPLMA and ELIMIPL directly utilize the softmax function for attention score computation. Results indicate that attention scores computed by MIPLMA and ELIMIPL better conform to the distribution of positive and negative instances.
In summary, our margin-aware attention mechanism yields more accurate attention scores. Moreover, when distinguishing negative instances within multi-instance bags proves challenging, our attention mechanism effectively mitigates their influence on bag-level features.
D.2 Comparison between Margin Loss and Margin Distribution Loss
To adjust the margin for the predicted probabilities in the label space, we first propose the margin loss Amt as shown in Eq. (9. Whereas the margin loss aims to maximize the mean margin of predicted probabilities, the margin distribution loss endeavors to concurrently maximize this mean margin and minimize the variance in these probabilities. Therefore, the margin distribution loss a generalized formulation of the margin loss. There are several significant connections between the two loss functions. (a) In scenarios where all Φi equal for {1,2, .m.. the variance
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MIPLMA
ELIMIPL
DEMIPL
0.10
0.06
0.08
0.03
0.06
0.04
Bag
0.04
0.02
0.02
0.00
10
20
30
40
10
20
30
40
10
20
30
40
(a)
(b)
(c)
0.08
0.10
0.04
0.06
Bag 2
0.08
0.03
0.05
0.02
0.03
0.02
0.01
0.00
0.00
0.00
10
20
30
40
10
20
30
40
10
20
30
40
(d)
(e)
(f)
0.06
0.06
0.03
0.04
Bag 3
0.04
0.02
0.02
0.01
0.00
0.00
0.00
10
30
40
10
40
10
20
40
(g)
(h)
Figure A2: Attention scores of MIPLMA, ELIMIPL, and DEMIPL for three test bags in the FMNIST- MIPL dataset with 1. The horizontal axis denotes the indices of instances, while the vertical axis represents the corresponding attention scores. Orange and blue colors indicate attention scores assigned positive and negative instances, respectively.
Table A5: The classification accuracies (mean= of MIPLMA and MIPL-MAMM on the benchmark datasets with the varying numbers of false positive labels (r E (1),,3))
Algorithm
MNIST-MIPL
FMNIST-MIPI
Hirdsong-MIPL
SIVAL-MIPL.
.9852.010
9151.016
.7761.000
.7031.026
MIPLMA
.979.014
.6711.028
.762:1.01
.6811.031
.749=.13
.64+..05
.746+.003
.627=.024
.987 ..08
88411002
.7621.015
.68211034
MIPL-MAMM
.999.015 015
8491.025
.7411.018
.28.1.017
.6521.061
.6711.028
.561
V(-) equals O. Consequently, the margin distribution loss reduces to the margin loss. (b) When the variability of Φ; is low, as indicated by small variance V(-). the margin distribution loss slightly exceeds the mean of margin loss M(.). This observation implies that when Φi exhibits minimal variation across different samples, the margin distribution loss marginally surpasses the margin loss. (c) When the variability of de is high, denoted by a large variance V..). the margin distribution loss Mf-J/(1-VEA can significantly exceed the mean margin loss M(-). In extreme cases, if V approaches 1, then the margin distribution loss becomes notably large. This suggests that when é; exhibits substantial variation across different samples, the margin distribution loss could notably surpass the margin loss.
To compare the magrin loss Lmi and the margin distribution loss Lyn we substitute the Lm in Eq. with Led. resulting in variant named MIPL-MAMM. Table(AS]presents the classification accuracies of MIPLMA and MIPL-MMNN on the benchmark datasets, where the weight of the margin loss L. is tuned to achieve preferable performances. MIPLMA outperforms MIPI-MMAMR in 10 of the 12 scenarios. On the MNIST-MIPL dataset, MIPLMA performs worse than MIPI--MAN AMM when r = and 3. We speculate that this discrepancy may be attributed to the model slightly overfitting due to
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the consideration of margin distribution on the relatively simple MNIST-MIPL dataset. Notably, the performance advantage of MIPLMA more pronounced on the more challenging Birdsong-Min MiIds and SIVAL-MIPL. datasets. This demonstrates that optimizing the margin distribution is more effective than only optimizing the margin mean, thus confirming the superiority of the margin distribution loss.
In summary, the margin distribution loss is associated with the variability of the margin loss across different samples, effectively indicating the concentration of the margin loss within dataset. In the MIPL datasets, the margin distribution loss outperforms the margin loss in most cases.
D.3 Robustness of the Weight for the Margin Distribution Loss
À denotes the weighting factor for the margin distribution loss. Figure A3 depicts the average accuracies from ten runs. On the Birdsong-MIP dataset, experimental findings encompass À values ranging from (2.3, ... 10). The obtained accuracy remains relatively stable when ranges between and 7. However, when exceeds 7, accuracy decreases. Therefore, is noteworthy that within certain range of X, MIPLMA can maintain stable performance the Birdsong-MIPL dataset. In the main experiment, set to 5 for the Birdsong- MMII dataset.
As shown in Eq.
0.8
accuracy
0.7
7 2 r
0.6
X
Figure A3: The classification accuracies (mean and std) of MIPLMA with varying À on Birdsong-MiPe dataset (r E (3)). The diameter of the circle represents the relative standard deviation.
D.4
Robustness of the Initial Temperature
describes the annealing process of the temperature parameter 7(), with the initial temperature parameter 7()) manually specified. To investigate the influence of different initial temperature parameters on MIPLMA, we vary the initial temperature parameter across {4, 5, 6, 7, 8,9, 10). while maintaining all other parameters constant.
Eq.
Figure A4 presents the average accuracies obtained from ten runs the MNIST (left) and Birdsong-Min MIPL (right) datasets. The experimental results suggest that the performance of MIPLMA remains relatively stable within the range of (4, 5. 6, 7, 8, 9, 10), particularly noticeable when r 1 and = on the MNIST-MIE dataset. However, MIPLMA exhibits fluctuations average accuracy when 3 on the MNIST-MILL dataset, and the standard deviation is significantly higher compared to when r = 1 and r = 2. The MNIST-MIPL is a five-class dataset, and r = 3 represents an extremely challenging scenario. Therefore, believe that the fluctuations in average accuracy observed in Figure A4 (left) are unavoidable. Furthermore, shown in Figure 1, when 7(1) varies within the range of to 10, the performance the Birdsong-Maa MIPL dataset does not exhibit significant changes.
In summary, the results presented in Figure A4 highlight the robustness of MIPI. variations in the initial temperature parameter within the range (5, 6, 7,8, 9, 10). In our experiments, the initial temperature parameter (0) consistently set to 5 for all datasets.
1.0
0.8
accuracy
accuracy
0.8
0.7
F r
F
r
0.6
0.6
5
10
5
8 9
10
(0)
(0)
Figure A4: The classification accuracies (mean and std) of MIPLMA with varying (()) E (4,5, 6, 7,8, 10) MNIST-MILL (left) and Birdsong-MIPL. (right) datasets (r (1,2,3)). The diameter of the circle represents the relative standard deviation.
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E Related Work
E.1
Multi-Instance Learning
45 and it has found applications in a variety of fields ranging from text classification [46 42). object detection [47], and video anomaly detection [48]. In contemporary multi-instance learning methodologies, a prevalent strategy involves incorporating attention mechanisms to aggregate features from each multi-instance bag into a unified feature representation, subsequently fed into classifier. Ilse et al. 4 introduced both the plain attention mechanism and gated attention mechanism to effectively enhance the performance of binary multi-instance learning, An extension of this paradigm is the loss-based attention mechanisms [4]] providing a solution for multi-class tasks. Owing to the exceptional performance of the attention mechanisms, multi-instance learning methods based on attention mechanisms have gained widespread adoption in tasks such as histopathological image classification [49 [5) These attention mechanisms typically fall under the category of soft attention mechanisms, wherein the weighted sum of attention scores for all instances in multi-instance bag yields a bag-level feature representation. Conversely, Li et al. (51) introduced hard attention mechanisms, which focus on selecting subset of instances from multi-instance bags to construct the feature representation.
learning has its roots in drug activity prediction
Despite the considerable performance advancements achieved by these algorithms in multi-instance learning tasks, their direct application in muuti-instannee partial-label learning scenarios is impeded by their inability to handle inexact label information directly.
E.2 Partial-Labe Learning
Partial-label learning has widespread applications in diverse real-world scenarios, encompassing facial age estimation (37), face naming [9]. object classification [52], and bioinformatics [29] 53 Margin
violations also exist in partial-label learning, prompting researchers to introduce the maximum margin criteria as a viable solution. Nguyen and Caruana (39 employed the maximum margin criterion to augment the distinction between the model's highest predicted probability on candidate labels and its highest predicted probability on non-candidate labels. Similarly, Yu and Zhang (38) focused on maximizing the margin between the model's s predicted probability on the true label and its highest predicted probability on labels other than the true one. However, these methods require an alternating optimization, contributing to the intricacies of the optimization procedure. In recent years, numerous deep learning- based partial-labe learning algorithms have emerged. Lv et al. B3) utilize linear classifiers or multi-layer perceptrons to learn feature representations from instances, employing progressive disambiguation strategies to identify true labels. Following this line of thought, Feng et al. 34 delved into the generation process of partial-label data and proposed two theoretically guaranteed partial-label learning algorithms. Similarly, Wen et al. (35) introduced weighted loss for disambiguation, serving as a generalized version across multiple algorithms.
While these algorithms exhibit considerable efficacy in tackling partial-label learning problems, they encounter limitations in directly handling inexact supervision within the instance space. Consequently, they cannot be directly applied to multi-instann muuli-instance partial-late label learning problems.
E.3 Multi-Instanm Partial-Label Learning
MIPL is an extension that encompasses both MIL and PLL. Its objective is to tackle the challenge of inexact supervision simultaneously present in both instance and label spaces. To our knowledge, only three viable MIPI algorithms, i.e., MIPI.GP [66. DRMIPL. (21), and ELIMIPI. [22). currently exist. Tang et al. [6 have introduced the MIPL framework, with MIPLGP adopting an instance-space paradigm. MIPLGP is structured in three steps. Firstly, it augments a negative class for each candidate label set. Secondly, it treats the candidate label set of each multi-instance bag as that of each instance within the bag. Finally, it employs the Dirichlet disambiguation strategy and the Gaussian processes regression model for disambiguation. On the other hand, DEMIPL follows the embedded space paradigm and consists of two steps. Initially, it aggregates each multi- instaiiestanne bag into unified feature representation through the disambiguated attention mechanism. Subsequently, employs a momentum- based disambiguation strategy to discern true labels from candidate label sets. Following this way, Tang et al. 222 have proposed ELIMIPL. to exploit the information from candidate and non-candidate label sets via three loss functions. Specifically, ELIMIPL learns the mappings from
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Table A6: Code availability of the algorithms.
Algorithm
URL
MIPLMA
/ttpp::/ggthubs Com/ tangu- Seu/MIPLMA
ELIMIPL
Sttps://githin com/ Langu- ELIMIPL
DEMIPI.
https://githut wtwptluuu.ooootaaaaee
MIPLGP
https://githin
com/ http://ghhuu...cmm//aaaaaeeaee/e
VWSGP
VGPMIL
LM-VGPMIL
MIVAE
ATTEN
ATTEN-GAA -ATE
LOSS-ATTEN
PRODEN
https//gftaub.co///rreee//////1
RC
LWS
/ http:///t..aaaoo//aaa/aaaaaaaaaa
CAVL
https///gthuu..cc///erreaas////
POP
https://g3thub..omm/paal./
PL-AGGD
attp://palm. seu. edu. cn/ 2hangml/
the multi-instance bags the candidate label sets and the sparsity of the candidate label matrix. Moreover, it incorporates the non-candidate label information via an inhibition loss. Recently, Yang al. 54 have proposed probabilistic generative model for multi-instance partial-label learning (MIPL) that infers latent ground-truth labels by modeling the data generation process of MIPL. From theoretical perspective, Wang al. (55) have established connections between MIPL and latent structural learning, as well as neurosymbolic integration.
However, the existing MIPL algorithms do not consider the margins of attention score and predicted probabilities, and thus suffer from the issues of margin violations illustrated in Figure
F
Data and Code Availability
The implementations of the compared algorithms are publicly available. Table A6 includes the URLs of all compared algorithms in this paper, while the source code of our proposed MIPLMA included in the supplementary material. The MIPL datasets can be accessed publicly hhtpp // //palm.. seu, edu. cn/ /t/cccn/aaaa /h/aanaggmm/
G Broader Impact
The proposed MIPLMA has several potential societal impacts, both positive and negative.
The positive impacts of MIPLMA include: (a) Medical diagnosis: MIPLMA could enhance the accuracy of medical diagnoses where obtaining exact labels is challenging. For example, in medical image classification tasks, when experts lack confidence in the provided results, MIPI.MA can yield more accurate outcomes. (b) Privacy-prest Privacy-preeerving surveys: Similar to PLL methods, MIPLMA can be applied in scenarios where respondents are hesitant to disclose sensitive information. By allowing respondents to select a set of candidate labels instead of providing a single label, MIPLMA can facilitate data collection while respecting privacy. This can be particularly useful in fields such as mental health, where patients may feel uncomfortable disclosing exact symptoms or conditions.
Conversely, there may also exist some negative impacts of MIPLMA. (a) Misuse in surveillance: There a risk that our our method could be exploited in surveillance systems infer sensitive information about individuals without their explicit consent. For example, an adversary could use our algorithm to analyze multi-instance data collected from social media or other sources to infer private details about individuals, leading to potential breaches of privacy. (b) Job displacement: As MIPI.M improves the efficiency and effectiveness of learning from inexact data, it might reduce the demand for human annotators. This could lead to job displacement involved in data labeling and annotation. Consequently, efforts should be made to retrain and upskill these workers to mitigate the impact.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The main claims presented in the abstract and introduction accurately reflect the paper's contributions and scope. The proposed MIPLMA algorithm and its advantages are substantiated by a detailed methodology and comprehensive experimental results.
Justification:
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: We discuss the limitations of the work in Section 4
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification: The paper provides the assumptions and a complete (and correct) proof in Appendix for each theoretical result in the main body.
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Answer: [Yes]
Justification: The paper provides the details of the implementations in Section and submit the source code of our algorithm in the supplementary material.
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Question: Does the paper provide open access the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification:
The paper provides the data and code availability in Appendix. We submit the code of our algorithm in the supplementary material and will make it publicly available.
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Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: We provide the source code and the experimental details as best we could.
7. Experiment Statistical Significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: Yes]
Justification:
The paper reports the mean accuracies and standard deviations from these ten
runs.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
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Answer:
[Yes]
Justification: We provide the details of the implementations in Section 3 including the computer resources.
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10. Broader Impacts
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Justification: The paper discusses both potential positive societal impacts and negative societal impacts of the work in Appendix
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Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
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Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
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Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
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Justification: This paper does not involve crowdsourcing nor research with human subjects. 15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA]
Justification: This paper does involve crowdsourcing nor research with human subjects.
24


Reviews:
[{"review_id": "vyCQpWVcpB", "reviewer": "Reviewer_H7jM", "summary": "The paper proposes MIPLMA, a new Multi-Instance Partial Label algorithm that focuses on margin adjustments in both instance and label space. While computing the bag level representations, it introduces a temperature parameter in the margin-aware attention mechanism to widen the gap between attention scores for positive and negative instances. It also employs a margin loss to maximise the margin between the highest predicted probability on the candidate label set and on the non-candidate label set.", "questions": "- The creation of the synthetic datasets is unclear. While Table 1 presents details regarding the number of instances in a bag, the distribution proportion of true positives in a bag is not mentioned. This could have implications for the performance of the algorithm.\n\n\n- In section 3.1.2 (line 200), It is mentioned that “PLL algorithms can be equipped with either the linear model or multi-layer perceptrons (MLP) as backbone networks”. However, DNNs like ResNet can be used to learn feature representations. Ideally, the comparison should be made using the same feature extractors, the ones mentioned in lines 208 - 213.\n\n- I would willing to reconsider my ratings post the author rebuttal", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Introduces and adapts margin-based approaches from weakly supervised learning paradigms for multi-Instance PLL\n\n- The paper is well-written, and I was able to understand it clearly", "weaknesses": "- In many cases, either the number of classes is low or the number of false positives is low, which raises concerns regarding the generalization of the algorithm\n\n- Paper has not been compared with some of the recent PLL algorithms like PICO [A], etc.  A discussion on why such comparisons were not done would be helpful. Maybe adaptation was difficult, or they focused on a single modality?\n\n- Contributions are limited, it is a direct adaptation of margin-based losses to the problem\n\n- The applications of this setup need to be more convincing to me. They do give one application on a Cancer detection dataset, however, I believe it could be achieved through an architecture capable of processing higher-resolution images. \n\n[A] Wang et al. PiCO: Contrastive Label Disambiguation for Partial Label Learning. ICLR 2022", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and valuable comments aimed at improving our paper. Below, we provide a summary of your comments along with our corresponding responses.\n\n> Concerns regarding generalization due to low class numbers or false positives.\n\nWe conducted additional experiments on the SIVAL-MIPL dataset with higher number of false-positive labels ($r \\in \\\\{4,5,6,7,8,9\\\\}$). The results in the following table show that MIPLMA achieves superior performance in all cases, which demonstrate its robustness to various number of false positive labels.\n\n| Algorithm |   $r=4$   |   $r=5$   |   $r=6$   |   $r=7$   |   $r=8$   |   $r=9$   |\n| --------- | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |\n| MIPLMA    | .618±.012 | .584±.019 | .524±.029 | .510±.030 | .431±.026 | .371±.051 |\n| ELIMIPL   | .559±.038 | .507±.022 | .439±.025 | .392±.031 | .343±.029 | .339±.030 |\n| DEMIPL    | .470±.036 | .413±.020 | .352±.032 | .323±.029 | .266±.022 | .275±.021 |\n\n\n\n> Lack of comparison with recent PLL algorithms like PiCO.\n\nWe did not compare our method with PiCO for two main reasons:\n\n- PiCO relies on data augmentation to learn diverse features of partially labeled images. However, the features of the Birdsong-MIPL, SIVAL-MIPL, and CRC-MIPL datasets are preprocessed tabular data. This makes it challenging to apply data augmentation-based PLL methods to these datasets.\n- The instances in the MNIST-MIPL, FMNIST-MIPL, C-R34-16, and C-R34-25 datasets are raw images, which allows data augmentation-based PLL methods to learn features for each instance. However, these methods cannot aggregate a bag of instances into a unified feature representation as they lack an attention mechanism or similar aggregation method.\n\nIn our submission, we compared our method with five PLL methods published between 2020 and 2022. We have now included an additional comparison with a PLL method called POP (ICML 2023). As shown in `Table R1` and `R2`  in the attached PDF, the main results indicate that while POP performs better than other PLL methods in most cases, it is inferior to our MIPLMA. We will include the above discussion and the results of POP in the revised manuscript.\n\n\n\n> A direct adaptation of margin-based losses.\n\nWe acknowledge that margin-based losses have been studied in existing methods in PLL such as PL-SVM and M3PL. However, these methods have two drawbacks that make them unsuitable for the MIPL problem: they rely on iterative optimization strategies, making them difficult to integrate with neural networks; furthermore, they only maximize the mean margin of the predicted probabilities. Our proposed margin-compliant loss integrates naturally easily with neural networks and simultaneously maximizes the mean margin while minimizing the variance of predicted probabilities.\n\nTo further illustrate this, we propose a variant of MIPLMA named MIPLML, which directly utilizes the margin loss $\\mathcal{L}\\_{\\text{ml}}$ (Eq. 9). Specifically, the loss functions of MIPLMA and MIPLML are $\\mathcal{L}=\\mathcal{L}\\_{\\text{d}} + \\lambda \\mathcal{L}\\_{\\text{m}}$ and $\\mathcal{L}=\\mathcal{L}\\_{\\text{d}} + \\lambda \\mathcal{L}\\_{\\text{ml}}$, respectively. The results show MIPLMA's superiority, demonstrating that a direct adaptation of margin-based losses achieve worse performance.\n\n| Algorithm |   C-Row   |   C-SBN   | C-KMeans  |  C-SIFT   |\n| --------- | :-------: | :-------: | :-------: | :-------: |\n| MIPLMA    | .444±.010 | .526±.009 | .557±.010 | .553±.009 |\n| MIPLML    | .424±.006 | .505±.007 | .544±.009 | .545±.009 |\n\n\n\n> Applications need to be more convincing.\n\nIn medical diagnostics, whole slide images (WSI) are high-resolution microscopy images of stained tissue slides. These images are extremely large, often reaching gigapixel size. Neural networks cannot learn an entire WSI due to their architecture and GPU limitations. Typically, WSIs are divided into smaller, lower-resolution tiles (multi-instances) to enable neural networks to learn meaningful features. In the future, we plan to collect higher-resolution WSI datasets to extend the MIPL applications.\n\n\n\n> The creation of the synthetic datasets is unclear. \n\nFor the MNIST-MIPL, FMNIST-MIPL, and Birdsong-MIPL datasets, we can control the distribution of true instances. The SIVAL-MIPL dataset, derived from the classical MIL dataset SIVAL, is created by adding false positive labels to each multi-instance bag. We calculate and report the proportions of positive instances in each bag, including their maximum, minimum, mean, and median values. This detailed distribution information will be included in the revised manuscript.\n\n|         | MNIST-MIPL | FMNIST-MIPL | Birdsong-MIPL | SIVAL-MIPL |\n| ------- | :--------: | :---------: | :-----------: | :--------: |\n| Maximum |    9.1%    |    9.1%     |     10.0%     |   90.62%   |\n| Minimum |    7.0%    |    7.0%     |     6.9%      |   3.12%    |\n| Mean    |    8.0%    |    8.0%     |     8.3%      |   25.6%    |\n| Median  |   8.11%    |    8.11%    |     8.3%      |   21.9%    |\n\n\n\n> The comparison should be made using the same feature extractors.\n\nFor the MNIST-MIPL and FMNIST-MIPL datasets, we use a two-layer CNN and a fully connected layer to extract instance-level features, which are then aggregated into a unified representation using the attention mechanism. The feature extractors for MIPLMA, ELIMIPL, and DEMIPL are consistent. However, the PLL methods cannot directly handle multi-instance bags or individual instances in MIPL because they lack of an aggregation mechanism to yield bag-level features and the candidate label sets are unknown during training. Therefore, it is not feasible to apply the same feature extractors across MIPL and PLLmethods.\n\n\n\nReferences:\n\n[B] Xu et al. Progressive purification for instance-dependent partial label learning. ICML 2023.", "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": "VCNq0ziqbW", "reviewer": "Reviewer_zb3e", "summary": "In this paper, the learning scenario of multi-instance partial learning (MIPL) is studied. The paper proposes a new approach for MIPL, whose key technical idea is margin regularization. The paper points out that an important issue for previous MIPL approaches is the ignorance of margin information in both the outputs of attention modules and final predictors, leading to incorrect attention weights and prediction confidence. To address this issue, the paper proposes a novel margin loss for MIPL, whose effectiveness is verified under various MIPL benchmark datasets.", "questions": "I suggest including more discussions on setting the parameter \\lambda. In my view, this parameter could significantly affect the learning performance. In my understanding, this parameter can be kept small at the beginning and be gradually increased. The reason is discussed in the paper: at the beginning, the model is less accurate, making the margin less informative, and the thing turns different as learning process proceeds. I find that currently, \\lambda is kept as a constant. So I would like to ask for more discussions on the interpretations for this choice.", "limitations": "Yes", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. In my view, the idea of margin regularization for MIPL is quite interesting, in special the regularization for the margins of attention weights. Even though attention weights are usually considered as a reflection of feature correlations, the accuracy of this correlation is difficult to quantify and receives less study. I think the margin measure is a good point for this quantification, and this idea can be indeed useful in the MIPL problem.\n\n2. The proposed method revives the technique of margin distribution optimization. I like this idea due to its elegance in quantifying margin information. This also leads to the future possibility of theoretical studies for MIPL since the margin distribution framework is well theoretical grounded. \n\n3. The experimental results are fruitful. The performance gain over baseline methods is significant.", "weaknesses": "1. The discussion on the scheme of modifying the strength of margin regularization can be augmented.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments and positive feedback on our paper. We are glad you found the idea of margin regularization and our results valuable. Below, we answer your questions.\n\n> More discussion on setting the parameter $\\lambda$. The parameter could significantly affect learning performance and might be better if varied during training rather than kept constant.\n\nTo address the concern about the parameter $\\lambda$, we conducted further study using a dynamic adjustment strategy defined as $\\lambda(t) = \\min\\\\{\\frac{t}{T^\\prime} \\lambda^\\prime, \\lambda^\\prime\\\\}$, where $t$ denotes the current epoch, and $T^\\prime$ and $\\lambda^\\prime$ control the rate of increase and the maximum value of $\\lambda$, respectively. We name this model MIPLMA-$\\lambda$. When $T^\\prime=1$, MIPLMA-$\\lambda$ is equivalent to MIPLMA. We conducted experiments on the MNIST-MIPL dataset with $T^\\prime$ set to $\\\\{1,10,50,100\\\\}$, using the same $\\lambda^\\prime$ as in MIPLMA. Results show that dynamically adjusting $\\lambda$ can sometimes improve classification accuracy compared to a constant $\\lambda$; however, in other cases it may perform worse. Notably, when $r=3$, MIPLMA-$\\lambda$ tends to enhance accuracy more significantly.\n\nWe sincerely appreciate your suggestion for dynamic adjustment of $\\lambda$. Designing effective adjustment strategies is indeed a valuable research direction. We will explore more refined dynamic adjustment methods for $\\lambda$ in the future.\n\n| Algorithm        | $r$  | $T^\\prime=1$ | $T^\\prime=10$ | $T^\\prime=50$ | $T^\\prime=90$ |\n| ---------------- | :--: | :----------: | :-----------: | :-----------: | :-----------: |\n|                  | $1$  |  .985±.010   |   .957±.056   |   .986±.007   |   .987±.009   |\n| MIPLMA-$\\lambda$ | $2$  |  .979±.014   |   .970±.015   |   .979±.011   |   .977±.014   |\n|                  | $3$  |  .749±.103   |   .763±.089   |   .753±.101   |   .738±.105   |", "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": "MeGZ5zUihY", "reviewer": "Reviewer_BX8M", "summary": "This paper deals with an emerging learning framework, i.e., multi-instance partial-label learning framework, which can be regarded as an extension and combination of multi-instance learning and partial-label learning. Overall, such dual inexact supervision makes the MIPLL difficult to resolve. This paper introduces a margin-aware attention mechanism and margin-compliant loss to deal with such issue.", "questions": "N/A", "limitations": "See the weakness.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The motivation is clear and straightforward;\n2. The paper is well-written and easy-to-follow.\n3. Empirical results show satisfactory performance on several benchmarks as compared with existing methods.", "weaknesses": "1. I am confused with the definition of MIPLL as the authors claimed in the paper: \"positive instances refer to the instances that belong to the true label in the setting of MIPL\"，I agree with the definition of positive instances. However, for the negative instances, \" while negative instances represent the remaining instances in the bag that are not associated with any label in the label space\". Does the author indicates that the negative instances are not associated with the whole label space or just the candidate label set for the given bag?  I think the authors should be careful with this definition. I don't think the whole label space is the correct description w.r.t the negative instances.\n2. The so called margin-aware attention mechanism and the margin-compliant loss are not novel, which in my opinion have already been utilized in other well-defined issues. So the novelty cannot reach the bar of NeruIPS.\n3. In addition, although the multi-instance partial label learning framework is relatively novel and interesting, there has already existed at least two pioneer works, which again decreases the novelty of this work. \n4. As compared with existing methods, the proposed algorithm cannot achieve the dominant performance on all the datasets under all the settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and constructive feedback. In the following, we address your comments and concerns.\n\n> Definition of negative instances.\n\nIn line with previous MIPL work such as MIPLGP, DEMIPL, and ELIMIPL, we do not associate negative instances with the label space. For the MNIST-MIPL dataset, positive instances are drawn from the target classes $\\\\{0, 2, 4, 6, 8\\\\}$, while negative instances come from the reserved classes $\\\\{1, 3, 5, 7, 9\\\\}$. Positive instances in a multi-instance bag are sampled from target classes, and negative instances are sampled from reserved classes. Therefore, the true label of a multi-instance bag corresponds to the actual label, with candidate labels sampled from the remaining target classes.\n\nLet us consider two scenarios:\n\n1. Negative instances not associated with the labels space: If a test multi-instance bag has positive instances from class $0$ and negative instances from $\\\\{1, 3, 5, 7, 9\\\\}$, correct classification requires the classifier to predict class $0$.\n2. Negative instances associated with the label space: If a test multi-instance bag has positive instances from $\\\\{0, 2\\\\}$ and negative instances from $\\\\{1, 3, 5, 7, 9\\\\}$, predicting either class $0$ or class $2$ is not considered wrong. However, this conflicts with the multi-class classification principle, which assumes each sample belongs to a single class.\n\nNot associating negative instances with the label space is reasonable for MIPL applications. For example, For the CRC-MIPL dataset, positive instances are cell types like lymphocytes and colorectal adenocarcinoma epithelium, while negative instances refer to background information, such as non-cellular areas or areas without significant tissue. If background is included in the label space, the true label for each multi-instance bag should reflect both the background and the cell type class, conflicting with the multi-class classification principle that each sample should belong to a single class.\n\n\n\n>  The margin-aware attention mechanism and margin-compliant loss are not novel.\n\nWe recognize that the attention mechanism and margin-based loss have been used in MIL and PLL, respectively. However, our paper introduces a novel perspective by identifying margin violations in both the instance and label spaces with a dual margin adjustment strategy. Existing margin-based PLL methods, such as PL-SVM and M3PL, have two main drawbacks: they rely on iterative optimization, making integration with neural networks difficult, and they only maximize the mean margin of predicted probabilities. In contrast, our margin-compliant loss integrates seamlessly with neural networks and both maximizes the mean margin and minimizes the standard deviation of predicted probabilities.\n\nWhile the attention mechanism and the margin-based methods are not novel in MIL or PLL, their effective integration and enhancement within the MIPL framework represent a significant contribution. We will elaborate on these unique aspects and improvements in the revised manuscript.\n\n\n\n> Existing works decrease the novelty of this work.\n\nExisting works, such as MIPLGP, DEMIPL, and ELIMIPL, provide important prior contributions to the MIPL framework. These can be divided into two categories: (1) MIPLGP uses a Gaussian Process Regression model for fitting transformed MIPL data, but it is sensitive to negative instances and requires more computational resources; (2) DEMIPL and ELIMIPL leverage attention mechanisms to learn global feature representations, yet they encounter margin violations in both instance and label spaces. \n\nOur work distinguishes itself by being the first to identify and address margin violations in MIPL. We propose an effective dual margin adjustment strategy to counter these issues. Additionally, we introduce CRC-MIPL datasets with deep feature extractors, specifically the C-R34-16 and C-R34-25 datasets, which is also a novel contribution in MIPL.\n\n\n\n> The proposed algorithm does not achieve dominant performance on all the datasets under all the settings.\n\nIn machine learning, the \"No Free Lunch\" theorem states that no algorithm can outperform all others across every task and data distribution. Table A4 in the Appendix presents the results of pairwise t-tests at a significance level of 0.05 between our proposed MIPLMA and the comparison algorithms. Out of $430$ comparisons, MIPLMA outperforms the comparison algorithms in $412$ cases and shows no significant difference in $15$ cases. Thus, while MIPLMA may not dominate in every scenario, it demonstrates substantial superiority in the majority of cases.", "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": "5MGEOmC0ma", "reviewer": "Reviewer_b79F", "summary": "In this paper, the authors observe the presence of ‘margin-violations’ in the MIPL problem, which manifest in two aspects: between positive and negative instances, and between candidate and non-candidate labels. Therefore, the paper proposes modifications to the attention mechanism and enhancement of the loss function to rectify and widen the margin.", "questions": "See weaknesses", "limitations": "NA", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. This paper aims to address a challenging scenario, specifically the MIPL problem, and identifies an intriguing phenomenon known as ‘margin violations’.\n2. This work proposes MIPLMA to solve this issue, which combines several effective but not complicated tricks.\n3. The authors conduct extensive experiments and compare with a number of methods to valid the effectiveness of the proposal.", "weaknesses": "1. In Figure 1, the authors compare the performances of MIPLMA and DEMIPL, however, according to the results in Table 2 and Table 3, ELIMIPL surpasses DEMIPL on FMNIST-MIPL, and both ELIMIPL and MIPLGP perform better than DEMIPL on CRC-MIPL-Row dataset, so the comparison with these two methods would be more beneficial.\n2. When discussing the linear-decay strategy for temperature in Equation 3, the sudden introduction of the constant coefficient 0.95 requires some clarification for better understanding.\n3. In Sec.3.1.1, the authors claim that they are the first to employ ResNet on CRC-MIPL dataset, so it is suggested that the authors should list the detailed baseline performances on C-R34-16 and C-R34-25 datatset bag generators, just as in Table.3.\n4. The authors assert that “features learned by ResNet-34 outperform those obtained via image bag generators in terms of classification performance.” However, it remains unclear whether this still holds on other benchmarks such as FMNIST-MIPL. To strengthen this claim, it would be beneficial to add further experiments or discussions on this.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your detailed and positive feedback on our paper. We are glad you found our approach to addressing ‘margin violations’ in the MIPL problem intriguing. Below, we address your comments and questions.\n\n> In Figure 1, a comparison with ELIMIPL and MIPLGP would be more beneficial.\n\nMIPLGP follows the instance-space paradigm, which assigns augmented candidate label sets of bags to each instance and aggregates bag-level labels from instance-level labels. As a result, we cannot observe the phenomenon of margin violations in the instance and label spaces. For ELIMIPL, we will update Figure 1 to include its results in the revised manuscript.\n\n\n\n>  The constant coefficient 0.95 in Equation 3 requires clarification.\n\nThe decay rate of $0.95$ for the temperature parameter in Equation 3 was selected based on preliminary experiments, which indicated that the performance of the margin-aware attention mechanism is relatively stable across a range of decay rates. To maintain consistency, we fixed the decay rate at $0.95$ for all datasets. We have conducted additional experiments on the FMNIST-MIPL dataset with decay rates from $0.9$ to $0.99$, as shown in `Figure R1` of the attached PDF. These experiments revealed classification accuracies between $0.907$ and $0.927$. We will provide these details and results in the revised manuscript.\n\n\n\n> List detailed baseline performances on C-R34-16 and C-R34-25 datasets, as in Table 3.\n\nThank you for your suggestion. The following table shows the classification accuracies (mean±std) for MIPLMA, ELIMIPL, and DEMIPL on the C-R34-16 and C-R34-25 datasets. MIPLMA shows a slight improvement over ELIMIPL and DEMIPL on the C-R34-16 dataset, and a more significant advantage on the C-R34-25 dataset. We will include more baseline performances in Table 3 of the revised manuscript.\n\n| Algorithm | C-R34-16  | C-R34-25  |\n| --------- | :-------: | :-------: |\n| MIPLMA    | .631±.008 | .685±.011 |\n| ELIMIPL   | .628±.009 | .663±.009 |\n| DEMIPL    | .625±.008 | .650±.010 |\n\n\n\n> It remains unclear whether the superior performance of features learned by ResNet-34 holds on other benchmarks such as FMNIST-MIPL.\n\nThe instance-level features of the Birdsong-MIPL and SIVAL-MIPL datasets are preprocessed, which prevents the use of DCNNs like ResNet-34 for feature learning. We have tested LeNet and ResNet-34 on the MNIST-MIPL and FMNIST-MIPL datasets, but the classification accuracies were unsatisfactory. We believe this is due to the relatively simple nature of the features in these datasets, which are adequately captured by simple networks like the two-layer CNN used in our study. While deep convolutional neural networks might perform better on fully supervised MNIST and FMNIST datasets, their benefits may be less evident in weakly supervised scenarios.", "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": "a5YJUMuGhD", "reviewer": "Reviewer_Unv6", "summary": "The paper proposes an approach to tackle the multi-instance partial-label learning (MIPL) problem. In MIPL, each training sample is represented as a bag of instances associated with a set of candidate labels. The existing MIPL algorithms may gives the high prediction probability on non-candidate label, which can lead to suboptimal performance. They propose MIPLMA  algorithm addresses this issue by introducing a margin-aware attention mechanism and a margin-compliant loss function. The algorithm dynamically adjusts the margins for attention scores, while the loss function ensures that the margins between predicted probabilities on candidate and non-candidate label sets are properly constrained. The paper presents experimental results on benchmark and real-world datasets that demonstrate the superior performance of MIPLMA compared to existing MIPL algorithms, as well as other multi-instance algorithms and partial-label learning algorithms.", "questions": "1. The initial weights are uniformly distributed. After the training in first epoch, the weights change. Subsequent epochs can only make the margin of the distribution larger, so the results are greatly affected by the first epoch. If there are errors in the training of the first few epochs, Will this error accumulate?", "limitations": "Yes.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-organized and easy to comprehend.\n2. The experiments are generally comprehensive.\n3. Theoretical analysis contributes to establishing the effectiveness of the proposed model.", "weaknesses": "1. The margin-aware attention is performed by setting a changing temperature parameter which seems a trival idea. And the details of how the gap of attention scores help the model to decrease the prediction probability on non-candidate labels are also not given.\n2. The form of margin-compliant loss seems not easy to be optimized in neural network framework, the paper should give more details on the proposed loss function.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough review and positive feedback on our paper. We are pleased that you found the paper well-organized, comprehensive, and theoretically supported. Below, we have summarized your comments and provided our responses accordingly.\n\n>  The details of how the gap of attention scores help the model to decrease the prediction probability on non-candidate labels are also not given.\n\nOur paper utilizes margin-compliant loss to increase the gap between prediction probabilities for candidate and non-candidate labels, thus reducing the probability for non-candidate labels. To examine the impact of attention score gaps, we introduce MIPLMA-NOTEM, a variant of MIPLMA without the temperature parameter in the attention mechanism.\n\nIn experiments with the MNIST-MIPL dataset ($r=1$), both models yield similar prediction probabilities of approximately 0.088 for non-candidate labels, showing no significant difference. Visualization of attention scores for three multi-instance bags from the training set, shown in `Figure R2` of the attached PDF, where the horizontal axis denotes the indices of instances, while the vertical axis represents the corresponding attention scores. Red and blue colors indicate attention scores assigned to positive and negative instances, respectively. The results reveals that the temperature parameter effectively increases the gap between attention scores for positive and negative instances.\n\nFurthermore, `Figure R3` of the attached PDF compares feature visualizations on the test set. MIPLMA produces well-clustered features by class, while MIPLMA-NOTEM exhibits errors, such as misclassifications where points from the first class incorrectly approach clusters of other classes. Therefore, while the temperature parameter may not directly impact prediction probabilities for non-candidate labels, it enhances feature representation accuracy. We will include these findings and discussions in the revised manuscript.\n\n\n\n> The margin-compliant loss seems not easy to be optimized in neural network framework. More details are required.\n\nThe core of the margin-compliant loss involves finding the maximum predicted probabilities for the candidate and non-candidate label sets, concatenating the margins, and calculating their mean and standard deviation. First, we use `torch.max` to obtain the maximum predicted probabilities for both sets. Then, we employ `torch.cat` to concatenate the margins in the current and previous batches. Finally, we calculate the mean and standard deviation of the margins using `.mean` and `.std` operations, followed by the value of the margin-compliant loss. All these operations, including `torch.max`, are differentiable in PyTorch. We will provide additional details on the margin-compliant loss in the revised manuscript to clarify its optimization within the neural network framework.\n\n\n\n> Will errors in the first few epochs affect subsequent training?\n\nIn MIPL, where prior knowledge of true labels in the candidate label sets is unavailable, initializing candidate label weights with averages is reasonable. Although early errors might accumulate and impact later training, our dynamic disambiguation loss reduces the influence of early predictions by assigning them lower weights, as shown in Eq. (8). To further investigate the effectiveness of our dynamic disambiguation loss, we also introduce a variant without the dynamic disambiguation, MIPLMA-$\\alpha$, where $\\alpha^{(t)}$ is set to 0 for all $t \\in \\\\{1,2,\\cdots,T\\\\}$.\n\nThe table below shows the performance of MIPLMA and MIPLMA-$\\alpha$ on benchmark datasets. MIPLMA-$\\alpha$ performs well on simpler disambiguation tasks (e.g., $r=1$ or $r=2$) but shows decreased performance compared to MIPLMA as the number of false positive labels increases (e.g., $r=3$). The gap between MIPLMA and MIPLMA-$\\alpha$ widens with greater disambiguation challenges. These results suggest that our dynamic disambiguation loss is effective iin mitigating the errors in the first few epochs.\n\n| Algorithm       | r    | MNIST-MIPL | FMNIST-MIPL | Birdsong-MIPL | SIVAL-MIPL |\n| --------------- | ---- | :--------: | :---------: | :-----------: | :--------: |\n|                 | 1    | .985±.010  |  .915±.016  |   .776±.020   | .703±.026  |\n| MIPLMA          | 2    | .979±.014  |  .867±.028  |   .762±.015   | .668±.031  |\n|                 | 3    | .749±.103  |  .654±.055  |   .746±.013   | .627±.024  |\n|                 | 1    | .991±.025  |  .897±.022  |   .772±.020   | .704±.022  |\n| MIPLMA-$\\alpha$ | 2    | .949±.056  |  .841±.021  |   .765±.027   | .658±.021  |\n|                 | 3    | .661±.161  |  .538±.053  |   .721±.042   | .586±.026  |", "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:
We sincerely thank the reviewers for their time and valuable feedback on our submission.

As we cannot resubmit the paper during the rebuttal period, we have attached a one-page PDF with additional experimental results for the new PLL algorithm POP on both benchmark and real-world datasets, results with varying temperature decay rates, and visualizations of attention scores and bag-level features. We will include these details in the revised manuscript.

Please let us know if further details or explanations are needed.

Meta-review: The paper introduces MIPLMA, a Multi-Instance Partial Label algorithm that aims to maximize margins in both instance and label spaces. In instance space, it leverages a temperature parameter in the attention mechanism to increase the gap between attention scores for positive and negative instances. In label space, it employs a margin loss that combines two objectives: maximizing the margin between the highest predicted probability in the candidate label set versus the non-candidate label set, and minimizing the variance of these margins across bags. Empirical results on four benchmarks and one real world dataset show satisfactory performance compared with existing methods.

The reviewers appreciated the margin based formulation of the problem and found the empirical results to be promising. The technical novelty is on the low side but overall the paper presents a well executed study showing the benefits of different margin objectives for MIPL problems.
Decision: Accept (poster)