A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Authors: Sebastian Schmidt
Leonard Schenk
Leo Schwinn
Stephan Günnemann
Venue: Accepted by TMLR ()

Abstract: In real-world applications of deep learning models, active learning (AL) strategies are essential for identifying label candidates from vast amounts of unlabeled data. In this context, robust out-of-distribution (OOD) detection mechanisms are crucial for handling data out-
side the target distribution during the application’s operation. Usually, these problems have been addressed separately. In this work, we introduce SISOM as a unified solution designed explicitly for AL and OOD detection. By combining feature space-based and uncertainty-
based metrics, SISOM leverages the strengths of the currently independent tasks to solve both effectively, without requiring specific training schemes. We conducted extensive experiments showing the problems arising when migrating between both tasks. In our experiments SISOM underlined its effectiveness by achieving first place in one of the commonly used OpenOOD benchmark settings and top-3 places in the remaining two for near-OOD data. In AL, SISOM delivers top performance in common image benchmarks.

Published in Transactions on Machine Learning Research (08/2025)
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt
ShhustionnS..chhiddf@ftm...
Technical University of Munich BMW Group
Leonard Schenk
Comnard.schenkk@ppiindd..o
Sprin-D
Leo Schwinn Technical University of Munich
L.chhwion@tumm.de
Stephan Günnemann
s.guernnerrannn@@Bamm.dd
Technical Universily of Munich
Reviewed on OpenReview: https: // openreview. net/ forum? id- HL 75La10FN
Abstract
In real-world applications of deep learning models, active learning (AL) strategies are essen- lial for identifying label candidates from vast amounts of unlabeled data. In this context, robust distr-iiiitiiiutiin (OOD) detection mechanisms are crucial for handling data out- side the target distribution during the application's 's operation. Usually, these problems have been addressed separately. In this work, introduce SISOM as a unified solution designed explicitly for AL and OOD detection. By combining feature space- and uncertainty- based metrics, SISOM leverages the strengths of the currently independent tasks to solve both effectively without requiring specific training schemes. We conducted extensive exper- iments showing the problems arising when migrating between both tasks. In our experi- ments SISOM underlined its effectiveness by achieving first place in one of the commonly usod OpenOOD benchmark settings and top-3 places in the remaining two for ncar-OOD data. In AL, SISOM delivers top performance in common image benchmarks.
1 Introduction
Large-scale deep learning models encounter several data-centric challenges during training and operation, particularly in real- world problems such as mobile robotic perception (Cai & Kout- soukos 2020) and autonomous driving (Nitsch et al. 2021). ). On the one hand, these models require vast amounts of data and labels for training, driven by the uncontrolled nature of real- world tasks. On the other hand, even when trained with exten- sive data, these models can behave unpredictably when facing samples that deviate significantly from training data (Schmidt et al. 2025a known as out-of-distribution (OOD) data.
1O0D
Unlsbeled
Labeled
nooD
Active learning (AL) addresses the first limitation by guiding the selection of label candidates. In the traditional pase-based
Figure 1: CIFAR-10 UMAP plot of unla- beled, near, and far OOD data compared to labeled data. For details, see Appendix B..
'Equal Contribution
Project Page with Code: https://wxu.. cit.it. wwt..ddddddnniiiii

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AL scenario (Settles 2010 models start with a small labeled training set and can iteratively query data and its labels from an unlabeled data pool. The selection is based on model metries such as uncertainty, diversity, or latent space encoding, One cycle concludes with the training on the increased labeled subset.
The second challenge, dealing with unknown data during operation, is typically addressed by OOD detection. OOD detection distinguishes between in-distribution (InD) data used for training the model and OOD samples, which differ from the training distribution. Literature differentiates between near-ODD and far- OOD, which can be categorized by the type of distribution shifts occurring. Yang et al. (2022); Zhang et al. 2023 define near-OOD
pure covariate shift, while far-OOD often contains a semantic shift.
Recent works in AL combined AL and OOD detec- tion into the Open-set AL (OSAL) scenario, assum- ing the existence of OOD data in the unlabeled pool, enabling a selection from the data pool without prior knowledge. However, existing methods (Ning al.)
Treining Phase: Active Learning
Operation Phase: OOD Detection
000 Ideniiication
Task Prediction
Uniabbladd Pool
Amnotator
ID
2022 Yang et al. 2023 Du et al.) (021) usually rely separate components for OOD separation and
Label Pool
Train Modil
data selection, respectively.
Figure 2: Real-world application life cycle comprising active learning in the training phase (left) and out-of- distribution detection in the operation phase (right).
As a result, the connection between both tasks has rarely been explored. Methodologically, both utilize common metrics, such as uncertainty, latent space distances, and energy. In addition, sample detected by such metries can be, on the one hand, novel AL sample that is insufficiently represented by the current training distribution. On the other hand, the sample can pose covariant shift in OOD setting. Considering both cases, as depicted in Fig, D shows an ambiguity and overlap of both sample categories. This raises the question whether an examination of the ambiguity of and relations between the respective samples can provide valuable insights for designing approaches for both tasks and OSAL, where current methods mostly consider both subtasks as independent.
Besides the methodology perspective, mobile robotic applications often train neural networks on recordings and afterward deploy them for operation shown in Fig. [ as an integrated life eycle. Given the amount of collected data, AL is applied for label-efficient training. During operation, OOD detection is employed to observe if the current operation is within the trained domain, which is necessary for real-world operation domains. Existing works address both challenges with separate methods that can lead to diverging goals like specific training schemes. Addressing these tasks scparately introduces significant overhead, especially for deployment and development, such as hyperparameter optimization or the training of auxiliary models.
Our work explores the connection between AL and OOD detection, introducing unified approach for both tasks that leverages their mutual strengths. Specifically, we propose Simultancous Informative Sampling and Outlier Mining (SISOM), which uses enriched feature space distances based coverage creating a symbiosis between AL and OOD detection. By exploiting the ambiguity of both tasks, SISOM achieves top performance across most near-OOD and AL benchmarks. By uniting both tasks, SISOM simplifies the application life cycle by eliminating the need for a separate OOD design phase and resolves conflicting design goals. This perspective contrasts with open-set AL, where OOD data incorporated into the unlabeled pool, forming combined task. Additionally, SISOM provides a novel latent space analysis for post-trainéng latent space refinement and first-offits-kind self-balancing of uncerlainly and diversily metrics. In summary, our contributions are as follows:
We propose Simultaneous Informative Sampling and Outlier Mining (SISOM), & novel method designed for both OOD detection and AL.
We introduce latent space analysis enabling an optimization loop for further post-training latent space refinement and a self-balanced uncertainty diversity fusion.
In extensive experiments, we demonstrate SISOM effectiveness in common image AL and OOD benchmarks against highly specialized state-of-the-art methods.
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2 Preliminaries
Active Learning: AL is subficld of machine learning designed to reduce the number of required labels by querying a set of new samples A of a query size q in cyclic process. Let X' represent a set of samples and y a set of labels. AL starts with an initially labeled pool L, containing data samples with features x and corresponding label y, and an unlabeled pool U where only x is known. However, y can be queriod from a human oracle. We further assume that LL and U are samples from a distribution Q2 In each cycle, a model f is trained such that f: Xr. + Dh. This model then selects new samples from based on a query strategy Q(x, f), which utilizes (intermediate) model outputs. As a result, the newly annotated set A is added to the labeled pool L'+1 and removed from the unlabeled pool U
Out of Distribution Detection: Ancillary, OOD detection assumes model f: Ai - Jh. trained on our training data {x. E L which have been sampled from the distribution Q2 During evaluation or inference, a model f encounters data samples & from a distribution é and Ω where 12 n = and x & L. Data sampled from S are referred to as InD data, while samples from e are referred to as OOD data. Based on the trained model f, a metric S is used to determine whether a sample x is sampled from Q or O.
InD if S(x 2 A
G(x, OODD if S(x: f) < A
(1)
OOD detection is further categorized into near- and far-OOD Zhang ct al. 2023 Far-OOD refers to completely unrelated data, such as comparing MNIST (LeCun et al.) 1998) to CIFAR- 100 (Krizhevsky et al. 2009,, while CIFAR-10 (Krizhevsky et al.] 2009) to CIFAR-100 would be considered as near-OOD.
OpenODD (Yang et al. 2022) ranks near-OOD detection as more challenging.
3 Related Work
Active Learning: AL mainly considers the pool-based and stream-based scenario Settles 2010), where data is either queried from a pool in a data center or a stream on the fly. For deep learning, the majority of current research deals with pool-based AL (Ren et al. (022)). However, further scenarios have been evaluated by |Schmidt & Günnemann (2023) and [Schmidt et al. (2024). Independent of the scenarios, samples are selected either by prediction uncertainty, latent space diversity, or auxiliary models. A majority of the uncertainty-based methods roly sampling- like Monte Carlo Dropout (Gal & Ghahramani 2016
employ ensembles (Beluch et al., 2018 Lakshminarayanan et al. 2017). To additionally ensure batch diversily Kirsch et al. (2019) used the joint mutual information. The uncertainty concepts have been employed and
further developed for major computer vision tasks, including object detection (Feng et al. 2019 Schmidt
et al. 2020), 3D object detection (Hekimoglu et al., 2022: Park et al. 2023b), and semantic segmentation (Huang et al. 2018). One of the few works breaking the gap between both tasks (Shukla et al. 2022) modified
an OOD detection method for pose estimation. Dutta et al. (2025), proposed to use imprecise probability by Imprecise Neural Networks for statistical verification of autonomous systems to enable safe operation and AL for reinforcement learning. Mukhoti et al. (2023) proposed DDU, uncertainty baseline based on spectral convolutions and Gaussian mixture models, which show improvements against other general uncertainty approaches on AL and OOD detection compared to other uncertainty approaches. Given the requirements of spectral convolutions, DDU is not flexible applicable for all use cases. In contrast, diversity- based approaches aim to select key samples cover the whole dataset. [Sener & Savarese (2018) proposed to choose a CoreSet of the latent space using a greedy optimization. Yehuda et al. (2022) selected samples having high coverage in fixed radius for low data regimes. Mishal & Weinshall (0024) extended the approach for more data regimes dynamic strategy mixing. Ash et al. (2020) enriched the latent space dimensions to the dimensions of the gradients and included uncertainty in this way. The concept of combining uncertainty with diversity has been further refined for 3D object detection (Yang et al.) 2024 Luo et al.) 2023). Liang el. al. (2024)
combined different diversity metrics for the same task. In semantic segmentation, Surprise Adequacy (Kim
et al.) (020) has been employed to mcasure how surprising a model finds a new instance. YY ct al. (2022)
used auxiliary tasks for unsupervised model training to select diverse samples. Besides the metric- based approach, the selection can also be made by auxiliary models mimicking diversity and uncertainty. These
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autoencoder-base based approaches (Sinha et al, 2019 Zhang et al.] 2020: Kim et al. 2021 and graph models Caramalau et al. 2021 to teacher-student approaches (Peng et al. 2021 Hekimoglu et al. 2024).
approaches range from loss estimation Yoo & Kweon 2019),
Outto--Disti Detection: To facilitate a fair comparison and evaluation of OOD methods, bench- marking frameworks like OpenOOD (Yang et al. 2022: Zhang et al. 2023) have been introduced, which categorizes the methods into preprocessing methods altering the training process and postprocessing meth- ods being applied after training. Preproccssing techniques include augmenting training data like mixing (Zhang et al. 2018 Tokozume et al. 2018) different samples applying fractals to images (Hendrycks ct al. 2022). Postprocessing approaches include techniques of manipulations on neurons and weights of the trained network, such as filtering for important neurons (Ahn et al.
2023 Djurisic et al. 2022], or weights
(Sun & Li 2022), or clipping neuron values to reduce OOD-induced noise (Sun et al. 2021]. Recent advance- ments in post-hoc network enhancement include SCALE (Xu et al., 2024), which enhances OOD detection
by activation scaling. Logit-based approaches encompass the model output to estimate uncertaintics using lemperature-scallngg (Liang et al. 2018), modified entropy scores (Liu et al. 2023), energy scores (Liu el al., 2020 Elftein et al. 2021) or ensembles (Arpit et al., 2022). Other methods use distances in the feature
space, such as the Mahalanobis distance between InD and OOD samples (Lec et al. 2018 rely on gradients
2020 Huang et a... 2021 Schwinn et al.) 2021). estimate densities (Charp- on latent space distances (Sun et al. 2022). Nearest Neighbor Guidance (NNGuide) (Park et al.) 2023a) refines classifier-based scores by respecting the data manifold's boundary geometry. A different branch operates on the features directly and evaluates properties
[Liang et al.)
2018: Hsu et al.
entier et al.
2020
2022] or apply k-nearest--eighhe
like the Norm Yuu et al., 2023 performs rank reductions via. SVD (Song et al.] 2022). NAC (Liu et al.,
2024 combined gradient information with density approach, where probability density function over InD samples is estimated. CombOOD (Rajaskkaaan et al. 2024] is pami-parametric framework that combines nearest-neighbor and Mahalanobis distances to improve OOD detection accuracy.
OpenSet Active Learning: The emerging field of OpenSet AL considers both tasks in one cycle, assuming the AL pool is polluted by OOD samples. Existing approaches (Ning et al., 2022 Park et al. 2022 Yang et al.) 2023 Safaei et al. 2024) present strong results by primarily tackling both tasks by seporate modules containing auxiliary models. As unlabcled and OOD samples are mostly considered decoupled with uncor- related modules, this field is orthogonal to examination of correlation and entanglement. Alternative approaches like SIMILAR (Kothawade et al. 2021 utilize submodular information measures to handle OOD data in unlabeled sets, adding computational overhead and needing initial access to OOD data, Additionally, Stojnić et al. (0024) expanded the open-sct classifier to an ensemble of models with an cxtra OOD class for semi-supervised AL. increasing computational demands. In contrast to works in this field, we investigate the correlation between unlabeled and OOD samples to provide a unified metric for both. We believe that this field benefits from the joint consideration of AL and OOD samples and an examination of their ambiguity.
While various works exist in OOD and AL, both tasks are considered independent. Even in OSAL, the tasks are mostly considered by independent method components. Some uncertainty methods are evaluated on both tasks but limit their evaluation to the uncertainty domain. Current state-of-the-art approaches are often specified for one task. In addition, the application life cycle consideration is unexplored.
4 Methodology
To address both AL and OOD detection tasks in a unified method to simplify real-world applications, we need to first understand the goals of these two tasks. AL aims to identify and select samples that are beneficial for training and increase the model's performance. These samples typically position themselves between the existing clusters in the latent space or near the decision boundaries. OOD detection targets the identification of data outside the training data and, therefore, outside the known clusters in latent space. Given the definition of far- and near-OOD, near-ODD is closer to InD data and located close to the decision boundaries and in between the existing clusters. Liu & Qin (2024) recently showed that OOD is generally closer to the docision boundary than InD, confirming this hypothesis. Fig. I investigates this hypothesis and shows the overlap in distribution of interesting unlabeled data and (near-) data compared to the distance to far-OOD samples.
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To target these overlapping regions, designed method that focused on the latent space regions between the clusters. To do so, SISOM employs an enlarged feature space Coverage (1) and increases expressiveness by weighting important neurons in Feature Enhancement (2). Based on this feature representation, we refine the AL selection and the InD and OOD border by using an inner-to-outer class Distance Ratio (3), guiding it to unexplored and decision boundary regions. As feature space distances are prone to poorly defined latent space representations, we introduce Feature Space Analysis (4), providing a self-deciding fusion of our distance metric with an uncertainty-based energy score. Optionally, our previous analysis enables us to optimize the Sigmoid Steepness (5), providing further refinement of the feature space representations from (2). An overview is depicted in Fig. 3 With this setup, SISOM specifically addresses both tasks without any requirements on the training scheme.
(1) Coverage: We aim to identify the regions of the samples that are interesting and unexplored for AL, as well as OOD samples in latent space. To do so, we rely on an informative latent space covering as much information as possible.
To increase the information gain, we cover the full network and define the feature space representation of input sample as concatenation of the latent space of multiple layers h; in a set of layers И in Eq. (
This follows the procedures of neural coverage (Kim et al. 2019 Liu et al. 2024) and is contrasting to most
diversity-based AL approaches (Sener & Savarese] 2018 Ash et al. 2020), which use a single layer.
z z= @ ... - hy (x) @ ... ha (x)
(2)
Given the feature space we further denote Zu as set of feature space representations of unlabeled samples from U, while Σ, denotes the set of representations of all labeled samples L.
(2) Feature Enhancement: To enhance the expressiveness of our defined latent space, improving class separation, we introduce a weighting of individual layers. Prior research (Huang et al. 2021 Liu et al., 2024) has demonstrated that the gradients of neurons with respect the KL divergence of the model's output and uniform distribution encapsulate valuable information for OOD detection.
We apply the technique to improve the features fur- ther and enrich these by representing the individual contribution of each neuron i, denoted as Ji- This gradient describes each neuron's 's contribution to the actual put being different from the uniform dis- tribution. A low value suggests that the neuron has little influence the prediction of given input sample. Conversely, if the value is high, the respec- tive neuron is crucial for the decision process.
Trained Model
SISOM Query:
(1) Coverage
s E L
(2) Enhance
|| 8'lla
Thus, the gradient vector can be interpreted as a saliency weighting for the activation values in the
@Dxz(ulS(aa o(s @
az
dout (3) Distance Ratio
dis
feature space to support separability. In detail, we
compute the gradient of the Kullback-Leibler (KL) divergence between uniform distribution and
(4) Feature Space Analysis
din r dest
f; min 1) Ex+
max Tany. 0)
the softmax output distribution f(x) for an input sample x with respect to the selected features z:
(5) Sigmoid Steepuess
A
@Dkr(ullfxx)) dz
OOD Detection
Active Learning
(3)
e(r) +1
arg (x))
FooD
We create a weighted fcature representation å by multiplying the calculated saliency element-wise with the feature space representation using the hadamard product and restrict to [-1, with the sigmoid function o:
Filter OOD samples and predict InD somples
Updote labeled set and retrain
Figure 3: SISOM framework for OOD detection and AL combined.
(4)
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InD
LaD
InD 200D
LD 1000
15-
Dousity
1000
Density
1OOD
Demeity
100D
Donsity
0-
0.0
0.5
1.0
-0.25
0.50
0.25
0.50
6.76
-05
0.0
0.5
1.0
1.5
(a) SISOM
(b) SISOMe
(c) SISOM + +OS
(d) SISOM + FOS+RS
Figure 4: Density plots for SISOM with energy, Optimal Sigmoid Steepness (OS) and Reduced Subset
Selection (RS) on CIFAR-100 with ncar-OOD (nOOD) and far-OOD (FOOD) as defined OpenOOD.
The resulting gredient-weighted feature representation effectively prioritizes the most influential neurons for each imput. This facilitates the identification of inputs activating atypical influence patterns, which is significant for AL as well as OOD detection. qualitative analysis demonstrating the effect of the feature enrichment given in Appendix A.4
(3) Distance Ratio: After we defined and enhanced our latent space, we design our metric identify the respective samples. Contrasting to other works in the latent space domain for AL and OOD detection (Sener & Savarose
2018). which rely on simple distance metrics, take inspiration from complex distance metrics (Kim el al. 2019) for detecting adversarial examples.
We assume the location of important samples is between the existing clusters in latent space. While far- OOD and curious AL samples are easier to detect due to larger latent space distances, ncar-OOD or AL samples close to the decision boundary are more difficult to detect and often more important. To identify samples in these regions, we rely on distance quotient between inner-class and outer-class distances, boosting the detection of samples close to the decision boundary.
The inner-class distance dn is defined the minimal feature space distance to known sample of the same class e as the predicted pseudo-class of the given sample. The outer-class distance dout represents the minimal feature space distance to a known sample of a different class than the sample's pseudo-class.
d (2)
min x (''zz(e''))
|| z'112
(5)
doat (z)
min
|| 2'112
(6)
x' 2'e (c(+t)
The distance is computed the gradient-enhaneed feature space a, defined in Eq. (4 with z' describing the nearest sample from the set of known samples Σ..
In many state-of-the-ars works on AL, computationally expensive distance calculations are often present (Sener & Savarese 2018 Ash et al. 2020 Caramalau et al. 2021). To make our approach more efficient for AL and feasible for large-scale OOD detection tasks, we select a representative subset T C En as a comparison set, thereby significantly reducing computational overhead. We utilize a fixed radius neighborhood to select samples, maximizing the coverage of the dataset within this radius in the feature space for each class. The effect of this subset selection is further investigated in Section 5
Our SISOM score reflects the distance between each neuron's 's weighted feature representation in the latent space and the nearest sample of the predicted class relative to the closest distance to sample from a different class for a sample x and the respective representation 2:
dnn dout
r(x)
(7)
An extended comparison of the different distance metrics and their ability separate InD and OOD is shown in Appendix A.4 while STSOM is depicted in Fig. 4al We further omit for after Eq. (8)
Since we calculated scale-value for each sample, follow the most commonly used top-k selection Yoo
& Kweon 2019 Kim et al. 2021 Gal & Ghahramani 2016) to select q samples with the highest distance
ratio with g being the AL query size:
A argmax argmax,r(x) x
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For OOD Detection, we map the distance ratios to an interval [0; 1] with the strictly monotonically decreasing function:
FOOD = 1 (() +1 2
(9)
(4) Feature Space Analysis: Having well-defined latent space is crucial for tISOM attain optimal performance. Furthermore, we hypothesize that techniques relying feature space metrics are more de- pendent on feature space separation than uncertainty- methods. This dependency is important for aISOM as utilizes a quotient of feature space metrics. Nevertheless, obtaining a well-defined and separable latent space may pose challenges specific contexts and tasks. To estimate the separability of feature space, we compute the average distance ratio Tang using Eq-
(4) and Eq. 7 for the known set as:
1 [L|
din(2 (%)
din + g)) dout (o e g))
Tang
(10)
dout (Z)
L
ZEL
zEL
A lower Tavg value indicates better separation of the samples in the enhanced feature space, implying that samples of the same class are relatively closer together than samples of different classes.
To mitigate possible performance disparities of iISOM difficult separable domains, we introduce a novel self-deciding process combining STSOM with the uncertainty-based energy score F((x
log logEE= (x)()(x);) estimated from the model's output logits f(x). We utilize Tavg to weights the combi- nation of uncertainly with our SISOM diversity metric as follows for an instance i:
f = min(rarg; 1) E. + max(1 Tavg: 0) -r-
(11)
The created SISOMe score f balances between uncertainty and diversity and relies either more on the energy score E or the distance ratio ri, based on the feature space separability. If Tavg - 1, indicating poorly separated classes, f; relies more on the energy score. Conversely, Tang + O, suggesting a well- separated feature space, f; relies more on the distance ratio. A density outline of our combined approach SISOMe is given in Fig. 4b Alternatively, one can replace Tavg with tuneable hyperparameter in Eq. (11)
(5) Sigmoid Steepness: Since Eq. (10) depends on the sigmoid function defined in Eq. 44, the sigmoid function has large influence on the enhanced feature space 2. An additional hyperparameter a can influence the sigmoid function's 's steepness. As z is concatenated from different layers in Eq. (2), the sigmoid can be applied to each layer j individually. This allows for a more nuanced control over the influence of cach neuron's ' contribution the final decision, and so influences the separability of the feature space. We define the sigmoid using the steepness parameter as:
(x)x) It aajz {ay: EZ Vj}.
(12)
Relating to Eq. 4 the set a of steepness parameters of the sigmoid function for each layer hys determines the degree of continuity or discreteness of the features within that layer. By applying a layerwise sigmoid, Eq. (4) is formulated as follows:
2 a (1i (x) O 9r.1) θ .......,.aaappi (x,(h((x) o (igg) ..... (xx o Og.n).
aD (DKL(ul)(x)) Vj.
with Bi.j
Th
Following this consideration we can select a values which optimize the feature space separability metric Tavg from Eq. [0) by minimizing Aopt = arg min. (a) Besides the quantitative assessment of our Feature Space Analysis and Sigmoid Steepness in Scction the influence of Sigmoid Steepness shown in Fig. 4c
5 Experiments
To evaluate the abilities of SISOM for the real-world application life cycle (Fig. D. we conducted compre- hensive experiments AL and OOD detection individually. We utilize the commonly used closed-set
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Unidded Selecte
Unideled Solacted
Uriateko Selecrd
(c) SISOM (ours) Figure 5: T-SNE feature space comparison of Loss Learning, CoreSet, and SISOM for SVHN on cycle 1.
(a) CoreSet
(b) Loss Learning
SISOM eflectively largets the areas in-belween the clusters.
pool-based AL scenario (Settles 2010 for AL. For OOD detection, we employ the extensive OpenOOD benchmark (Yang et al.) 2022; Zhang et al. 2023). In addition, we conduct OOD detection using models trained by AT to evaluate the whole application life cycle steps from Fig. 2
An evaluation of the OSAL scenario, where OOD data is present in the unlabeled AL pool, is out of scope in this work. Existing OSAL (Ning et al., 2022 Park et al.) 2022 Yang et al. 2023 Safaei et al., 2024) approaches employ two components, one for each task, while this work focuses a single component that solves both based on an ambiguity. This ambiguity would confuse the proposed method when applied independently to the compound lask. In addition, the OOD filtering components oflen require access lo OOD samples, which is not permitted in the classic OOD detection setup. However, a recent work (Schmidt) ct al. 2025b effectively integrated SISOM with additional components into a framework addressing OSAL.
Latent Space Assessment: In Fig. I we compare near- and far-OOD data as well as unlabeled data with the labeled data. It can be seen that the near-OOD data and the unlabeled data are positioned close to the individual clusters of the InD labeled data. This confirms our hypothesis that near-OOD can be ambiguous to unlabeled data. Moreover, in real-world applications, the model is more likely to encounter semantic shifts along with contextually similar data, like Tiny ImageNet (Le & Yang. 2015) to CIFAR-10 (Krizhevsky' et al. 2009 instead of contextual shifted far-OOD data like MNIST (LeCun et al. 1998 to CIFAR-I0.
To validate the assumptions made in Section 4 for AL, we analyze the latent space configuration in our AL experiments. The objective of our method is to select samples in the decision boundary region for the AL case. In Fig. 5 we compare CoreSet and Loss Learning with SISOM. CoreSct shows high diversity in unseparated regions. The pseudb-uneertaaint--eess
Loss Learning is concentrated in ils selection but fails to diversify the selection across all decision boundaries. In contrast, SISOM, as depicted in Fig. [c effectively targets the decision boundary and covers the area between unsoparated samples, demonstrating its effectiveness for AL and near-OOD.
Active Learning: AL exists in numerous variants; given the focus on showing the relation between OOD detection and AL, we adhered to common close-set AL benchmark settings (Yoo & Kweon 2019 Ash et al. 2020) with the in this context widely evaluated datasets, namely CIFAR-10 and CIFAR- 100 Krizhevsky et al. 2009), as well as SVHN (Netzer et al., 2011), in conjunction with different ResNet models (e et al. 2016). Since we assume a complete learning life-cycle, we mostly focus on the high and mid data regions LLith et al. 2023), as low data regions would not allow reasonable performance for deployment. We selected our query sizes accordingly and followed the commonly suggested sizes of q = 1000 for CIFAR-10 (Yoo & Kwcon 2019 Lüth et al. 2023) and q = 2000 for CIFAR-100 100 (Caramalau et al. 2021). We employ several baselines, including CoreSet (Sener & Savarese 2018), CoreGCN (Caramalau et al.) 2021 Random, Badge (Ash et al.) 2020), and Loss Learning (LLoss) (Yoo & Kweon 2019). Additionally, we adapted
NAC [Liu et al. 2024) from OOD detection to AL to assess the transfcrability from OOD to AL.
Since more recent. AL methods use pre-text tasks or unsupervised weights to select samples, we further consider an additional Semi-Supervised AL setup for a fair comparison, including more recent approaches. In this setting we compare SISOM with TypiClust (Hacohen 2022) and PT4AL (Yi et al. 2022). Additionally, in this setup, SISOM can profit from the well-defined feature space. All experiment details, including parameters and settings, are in Appendix B.
al.] 2022). ProbCover (Ychuda et al.
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CIFAR-10: In the CIFAR-10 benchmark depicted in Fig. ba SISOM and SISOMe demonstrate rapid progress and consistent performance, surpassing other methods in all selection cycles. Furthermore, as the sample size increases, our method maintains its superiority over Learning Loss and CoreSet. NAC does not demonstrate superior performance compared to Random. To underline the versatility of SISOM, we analyze additional mid-dala regimens and different query sizes as proposed by Lüth al. (2023) Appendix A.3 the mid data regime in Fig. 10a In this data regime, SISOM and SISOMe maintain their performance shown the high data region. As SISOM does not involve continuous distance updates and applies a top-k selection, it could suffer from less diversity in the selected batch. To investigate this problem, perform additional experiments with reduced and increased query sizes in Fig. Da and Fig. [9b (Appendix A.2). respectively.
SISOM's high performance in both settings strengthens our hypothesis that targeting the areas between clusters leads to higher batch diversification than purely uncerlainty-basee approaches.
CIFAR-100: After examining SISOM in datasets with limited number of classes, we examine the AL setup the larger CIFAR-100 datasel and port the results in Fig. 6b In this setting, all meth- ods are less stable in their ranking compared to the other dataset, reflecting the increased difficulty of the dataset. The complexity of the dataset re- quires more data for the model to perform effec-
Relative Accuary [%pp..]
SISOM (ours)
P4AL
tively. While in the early stages, pure diversity- based methods ity in the last selection steps and achieves the high- est performance difference only in the last step SI- SOMe more effective.
Badge
in the lead, STSOM gains veloc-
Probcover
Typiclust
CoreSet
Random
2
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
Somi-Supervised: In Fig. 7 we compare different ap- proaches designed for semi-supervised AL setup. For this experimental setup, every approach builds upon the unsupervised pro-training weights, which are required for TypiClust and ProbCover. While both approaches do not perform strongly in the clas- sic data regime, PT4AL shows a good performance by leveraging training on auxiliary pre-tasks. However, SISOM strongly profits from the pre-training and outperforms PT4AL without additional pre-task training.
training data [%)
Figure 7:
Comparison of accuracy relative to ran- dom selection of active learning methods on a semi- supervised CIFAR-10 setup with indicated standard errors for ResNet 18.
2
Relative Accuary in (%kpt.
Relative Accuary in [9spt.]
2
SISOM (ours)
SISOM (ours)
SISOMe (ours)
SISOMe (ours)
Badge
-2
Badge
Coreset
Coreset
CoreGCN
CoreGCN
LLass
LLoss
-2
NAC
NAC
Random
Random
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
0
5
10
15
20
25
30
35
40
training data [%)
Used training
(%)
CIFAR-10
(b) CIFAR-100
Figure 6:
Comparison of different active learning methods CIFAR-10 and CIFAR-100 with ResNet18
with indicated standard errors. Relative accuracy is the absolute performance difference between met and random selection.

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Published in Transactions on Machine Learning Research (08/2025)
Following the experiments on CIFAR-10 and CIFAR-100 conduct experiments on SVHN and report them in Appendix A.1
SVHN:
Additionally Active Learning Experiments: To underline the flexibility of SISOM, we conduct additional experiments on different models. Appendix A.3 and Fig. show experiments with a larger ResNet version. The larger model leads to lower performance differences between all methods. STSOMestruggles the early cycles with latent space assessment but eclipses others and SISOM after few cycles. Finally, SISOM and SISOMe achieve the highest accuracy in the final cycles.
In all AL experiments, SISOM achieved state-of-the-art performance across all three datasets, confirming its viability for AL. Although SISOM initially lags behind on CIFAR-100, it surpasses other methods in later selection cycles as more training data improves fealure space separation.
Out-oo-Distributiio Detection: Following the classic AL benchmarks, we utilize the OpenOOD frame- work (Yang ct al.)
2022; Zhang ct al., 2023) evaluate on the OOD detection task. We employ the recommended benchmarks on CIFAR-10 (Krizhessky et al., 2009), CIFAR-100 (Krizhevsky et al. 2009) and ImageNet 1k (Deng et al.) 2009) following the framework's OOD datasets categorization (Yang et al. 2023) (provided in Appendix B.2). For a fair evaluation, we use the official leader board, Cross- Ennsssyyyypyy training checkpoints, and report near- and far-OOD results. The results are reported as aligned to the framework without standard deviation. In addition, we evaluate the life cycle setting. For all experiments, we employ the implementation provided by the OpenOOD framework when it is available. The basclines used for validation include NAC (Liu et al., 2024). Ash (Djurisic et al.) 2022). KNN (Sun et al.,
2022 Zhang ct al.
2022), Odin (Hsu et al.
2020): ReAct. (Sun et al.
2021 3 MSP (Hendrycks & Gimpel 2016). Energy
LLu et al.
2020); Dice (Sun & Li, 2022), SCALE (Xu et al. 2024), CombOOD (Rajasckaran et al.
2024h, NNGuide (Park et al. 2023a). RankFeat (Yu et al. 2023), FeatureNorm (Song et al.) 2022) and
GEN (Liu et al.) 2023) and were selected based on their near-OOD performance. Moreover, we ported the
CoreSet (Sener & Savarese 2018) AL method to verify the transferability from AL to OOD.
In Table D we examine the performance of SISOM and SISOMe for the three benchmarks. As stated before, the near-OOD data is more relevant for this task. We focus on the near-OOD evaluation and report the far-OOD results in Appendix [A.5] We rank the methods by their combined rank over the three benchmarks (Appendix B.2 For the CIFAR-10 benchmark, SISOMe and achOMac the highest AUROC score for near-OOD data, respectively. In the CIFAR-100 evaluation, SISOMe ranks as the third-best method for near-OOD) and stands apart from the individual metrics, SISOM and Energy. GEN, which shows weaker performance in the other sctting, achieves first place. This is an interesting finding since, in contrast to CIFAR-10, Energy achieves better performance than SISOM among the individual metrics on 100.R-100. This supports our hypothesis that we obtain stronger performances in well-separated and poorly-separated feature spaces by considering the average ratio Tavg as a proxy for feature separation. The Image.Net 1k benchmark suggested by OpenOOD contains more classes and is much larger dataset than the previous ones. In this setup STSOMe achieves the third-best scores on near-OOD, making it the only method with three thpe rankings. In addition, when aggregating the individual ranks across the three benchmarks,
Table 1: Near-OOD AUROC benchmark for CIFAR-10, CIFAR-100, and ImageNet with Cross-Entropp training. Dataset and official checkpoints according to OpenOOD. It can be seen that most methods focus on one particular setting. Given the number of baselines for each dataset, we estimated a rank for each benchmark and aggregated them for an overall ranking reported in the first row. The top three ranks are marked in gray from dark to light, with the top method name also in bold. For individual datasets, the top three ranks are indicated from first to third using bold, double underlined single underlined formatting.
SISOMe
Fead. Norm
RankFout Font
CombOOD
MVGuide
SISOM
NAC
KNN
ConoSet
GEN
MSP
EnerAY
ReAct
ODIV
KLM
DICE
ASH
SCALE
Dataset
Overall Rank
14
8
7
16
8
CIFAR-IN 10
91.76
91.40
90.93
90.64
90.34
88.20
86.03
87.58
87
85.52
82.87
79.46
79.19
78.34
75.27
82.55
91.13
87.56
CIFAR 100
81.10
79.42
75.90
80.18
75.69
81.31
80.27
80.91
80.77
47.87
79.90
61.88
76.56
79.38
78.20
80.99
78.77
81.25
ImageNet 1k
78.59
77.33
74.43
71.10
76.83
76.02
76.03
77.38
67.57
74.75
50.99
76.64
73.07
78.17
81.36
95.22
73.57
10

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Published in Transactions on Machine Learning Research (08/2025)
Table 2: OOD benchmark for CIFAR-10 using the AL checkpoints of SISOM
AUROC
SISOMe
ReAct
GEN
MSP
ASH
NAC
RankFeat
ncar-OOD
86.84
86.84
85.43
84.37
83.39
82.26
60.20
far-OOD
88.39
87.72
86.04
84.85
87.33
85.06
56.73
SISOMe ranks first. Interestingly, while NAC showed high performance in CIFAR-10, it ranks much lower, and KNN, the third-best method in CIFAR-10, ranks last. Mcanwhile, ASH, which ranks forth in this benchmark, ranked last. in the CIFAR-10 setting. This underlines the difficulty of the different. datasets and their benchmarked OOD pairs. In the real-world applications, less relevant far- B00-O0 evaluation in Appendix A.5 the dependence on the performance between datascts and methods persists. Despite this, SISOMe and SISOM achieve high scores, with SISOMe remaining the only method with a top-3 ranking in all benchmarks, leading to an overall top-one rank.
To evaluate the effectiveness of SISOM a life cycle setting, we conduct OOD benchmark experiments with models trained in an AL cycle. We use the same setting as for the benchmark CIFAR-10 experiments with similar near- and far-OOD. The exact setup and training scheme of the checkpoints is described in Appendix B In Table 2] all methods suffer from less training data. However, SISOMe the top performance for both OOD categories, making it suitable for the full application life cycle. A more detailed analysis, including different intermediate cycles, is shown in Appendix A.6
Full Life Cycle:
Overall benchmarks, iISOMe the only approach, being consistently under the top three ranks and secured first. place in the overall ranking. Excluding SISOMe, SISOM achieved one top-one ranking. As intended, our method performs relatively better on near-OOD data than on far-OOD data. This is under- standable, as the ratio between inner and outer class distance is higher for data close to the training data distribution while the quotient is lower for far-OOD. Additionally, near-OOD is closer to the data of interest for AL selection. According to Yang et al. (2022), near-OOD is considered the more challenging task and is more likely to occur in real-world applications. Thus, a higher score on near-OOD may be preferred in practice.
Ablation Studies: In an ablation study, we examine the effect of unsupervised feature space analysis and reduce the labeled set T. A study of the individual components of SISOM is given in Appendix A.4
Optimal Sigmoid Steepness: In our feature space analysis in Section 4 we derived ravg in Eq. (10) as proxy for the feature space separability. Due to the distance concept of SISOM, we hypothesize that it works better in well-separated feature spaces. To examine this, we conduct random search for different sets and record the different Tavg values. To reduce the search space, we follow the premise postulated in Section 4 that generally, deeper layers require a steeper sigmoid curve, i.e., a higher a value due to the nature of the features captured within these layers. After computing every ravg value for cach combination of a, we select the Copp set that minimizes ravg. Formally, this can be written as:
Cppt = arg min (a)
a
In Table 3 an optimized set aopt is marked with OS. As it can be seen, a set with better feature space separation leads to increased performance for CIFAR-100 and ImageNet, partly confirming our hypothesis. In CIFAR-10, however, the original set of parameters yields the best results. One explanation might be that, in CIFAR-10, the different classes are already well scparated, such that optimization on this scparation yields no improvement and leads an overfitting behavior.
Reduced Subset Selection; For larger datasets, distance-basee approaches like CoreSet (Sener & Savarese 2018) and Badge (Ash et al.) 2020) face significant computational challenges, impacting OOD detection. In Section 4 we proposed using a reduced subset T of the comparison set Z,,. For each datasel, we select a total of 10% of the samples for each class, drastically increasing inference speed. The impact of our reduced subset selection (RS) is compared in Table DaleBann and illustrated Fig. 4d The preprocessing steps for SISOM in Table 3 show improved AUROC near- -OD scores for all datasets, supporting our earlier hypothesis. While feature analysis and pre-selection enhanced performance for ImageNet and CIFAR-100, CIFAR-10 did not
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Published in Transactions on Machine Learning Research (08/2025)
Table 3: Ablation study on Optimal Sigmoid Steepness (OS) and Reduced Subset Selection (RS) on near
OOD benchmarks.
ImageNet
CIFAR 100
CIFAR 10
Method
ACROC,
Tavg
AUROC,
Tavg
AUROC
Tavg
SISOM
77.21
0.270
75.93
0.33
91.33
0.26
SISOM, OS
77.4
0.266
79.56
0.19
90.37
0.099
SISOM, RS
77.33
0.249
76.07
0.31
91.40
0.24
SISOM, OS, RS
77.37
0.245
79.69
0.18
90.54
0.086
Table 4: Runtime comparison of different. subset. selection sizes for STSOMe seconds. The setup time is the time to build up the for comparison, while eval time measures one run over all OpenOOD evaluation datasets for CIFAR-100.
Subset Size T
100%
60%
20%
5%
Setup Time [s]
291.17 + 1.14
60.87 = 0.93
40.63 + 0.79
14.53 + 0.92
Eval Time [s]
885.67 67.66776 + 3.31
688.34 + 6.27
376.57 + 1.60
251.67 + 0.74
AUROC near OOD
80.97
81.01
81.03
81.10
show similar improvements. Considering the low average relevance score, the selected values may have overly constrained the feature space, resulting in overfitting behavior. Further details in Appendix B.3
Runtime Considerations: In Table 4 we compare the runtime of SISOMe for different subset sizes for full OpenOOD CIFAR-100 evaluation. It can be seen that with decreasing subset size, the computational efficiency increases for setup and evaluation time. Perhaps surprisingly, even the performance is slightly improving with decreasing subset size. One reason might be that a smaller subset is more compact, outlicr-free representation of the data distribution. We also compare SISOM's 's and SISOMe 's runtime other OOD detection and AL methods in Appendix A.6 In general, approaches without distance calculations are faster than diversity- based methods such as SISOM.
6 Conclusion
We proposed SISOM, the first approach designed explicitly to solve OOD detection and AL. jointly, providing effective simplification in real-world application life cycles by eliminating an additional OOD detection design phase and avoiding conflicting goals of AL and OOD detection. By weighting latent space features with KL divergence of the neuron activations and relating them to the latent space clusters of the differ- ent classes SISOM achieves state-of-the-art performance in both tasks, without requiring a specific training scheme. addition, SISOM provides a novel leature space analysis scheme enabling a post-training feature space refinement as well as self- balancing uncertainty and diversity fusion introduced SISOMe. Our experiments show that unlabeled samples and ncar-OOD data be ambiguous, which SISOM can leverage. In the common OpenOOD benchmarks, SISOM achieves the top-1 performance in one of the three bench- marks and is the only method with top-three places in all benchmarks for challenging near-OOD) scenario. For AL, SISOM surpasses state-of-the-are approaches in eight different benchmark settings. While current state-of-the-art approaches are highly specialized for either AL or OOD detection, SISOMsolves both tasks with the same approach. This versatility makes SISOM well-suited for real-world applications, like envi- ronment sensing, which face high label costs, abundant unlabcled data, and OOD samples inference. In addition, SISOM provides important insights into the ambiguity of unlabeled and near-OOD samples.
In future work, aim to leverage the relation of near- MOD data and unlabeled samples to explore tasks setup like open-set AL where currently two components are required. In addition, we aim extend SISOM to more complex tasks and investigate the elfect batch diversification techniques on our two task approach.
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Published in Transactions on Machine Learning Research (08/2025)
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0.85
0.9
0.75
0.8
Accuary [%)
0.70
Accuary in
0.65
(aurs)
0.7
SIS0M (ours)
SISOMe (ours]
SISOMe (ours)
0.60
Badge
Badge
Coreset
Coreset
CoreGCN
CoreGCN
0.55
LLoss
LLoss
Random
Random
0.5
15 Used training data in [%6]
25
Used training data in [%6]
(a)
500.
(b) 1500.
Figure 9: Comparison of different query sizes for CIFAR-10 with ResNet18. It can be seen, that SISOM and SISOMeare quite robust for variation of the selection size.
A
Additional Experiments
In this section, we present additional experiments for STSOM.
A.1 Active Learning SVHN
Following the CTFAR experiments settings, we de- pict the results for the SVHN experiments in Fig, 8 with query size of q 500. Similar to the CIFAR- 10 results, mance, but the method differences shrink with the easier the dataset. In the last cycle, SISOM reaches the highest performance, with a margin over other methods. As for CIFAR-10, NAC did perform well in the data selection. Given that SVHN's 10 classes numbers, it is easier than the more di- verse CIFAR-10 benchmark dataset. This can be obscrved by an overall reduced performance gap be- tween the methods compared to CIFAR-10. LLoss starts with a strong performance, which can be ex- plained by the additional loss of learning loss and network extensions.
SISOM (ours)
SISOMe (ours)
Badge
method maintained high perfor-
Relative Accuary in [%ppt]]
Coreset
CareGCN
LLoss
Random
-2
2.5
5.0
7.5
10.0
12.5
15.0
17.5
Used training [%)
Comparison of accuracy relative to ran- dom selection for different active learning mothods on SVHN with indicated standard errors.
Figure 8:
A.2 Query Size Analysis
The query size is an important design factor of AL experiments with mcasurable impact on the performance of methods Lüth et al. 2023 To investigate how large and small query sizes impact the performance of SISOM, we conduct experiments with 500 and 1500. In Fig. 9a with a small query size, SISOM and
SISOMe can increase their performance lead compared to other methods. For a larger query size (Fig 9b the differences shrink as the larger selection size leads to faster model convergence.
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0.70
0.65
0.60
Accuary in [96
Actuaty
0.55
0.7
SISOM (ours)
SISOM (ours)
0.50
SISOMe (ours)
SISOMe (ours)
Badge
0.6
Badge
0.45
Coreset
Coreset
CoreGCN
CoreGCN
LLoss
0.35
Random
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0 22.5
Used training
training in [%6]
(a) CIFAR-10 mid data regime.
(b) ResNet34 CIEAR-10
Figure 10: Additional experiments for active learming, with a) CIFAR-10 in the mid range data regime and b) ResNet34 for high data regime aligned with our experiments in Section 5
A.3 Further Active Learning Experiments
CIFAR-10 Medium Data Region: In addition to our primary high data region experiments in the main paper we conduct experiments the medium data region in Fig. 10a In this setting, the initial set size and the query size are to 250, as recommended by Lüth al. (2023) can be seen that SISOM can keep its good performance in this scenario well, while in the early phase, the selection of SISOM targets the right samples. The less defined latent space lets it leak behind SISOMe cycle 2. In general the performance differences are more inconsistent compared to the high data region.
Additional Models: In addition to our experiments in the main paper, we verify the effectiveness of SISOM for different models. In Fig. TOb, we see the performance of all methods converges fast given the larger model. However, in the last cycles, both SISOM Semain on the top of the ranking.
A.4 Feature Space Assignments
In this section, we highlight the influence of major components of our methods the ability to separate InD and OOD data. In Fig. 11 we display the influence of the KI. divergence gradient with a T-SNE analysis on CIFAR-10 (Krizhessky et al.) 2009) as InD and Tiny ImageNet (tin) (Le & Yang 2015 as near-OOD, Without feature enhancement, the latent space much harder to separate, and tin is distributed all over the latent space as shown in Fig. [la In contrast, the latent space with KL divergence enhances features, is much more separated, and has a clearer decision boundary the near classes as indicated in Fig. 11b
In addition to the previously presented density plots, we show the inner and outer distance together with the distance quotient of SISOM in Fig. 12 for CIFAR-10. Fig. 12a shows the inner class, indicating small inner class distances leading to good scparability for the InD data. On the other hand, the outer class distance in Fig. 12b provides a good separable peak for InD data, but a portion of InD overlaps with OOD data. The combined distance quotient shows the increased scparability of the different InD and OOD sets as depicted in Fig. 12c
A.5 Extended OOD Benchmark Results
In Table 5 we present the results of far-OOD along with near-OOD results of the three benchmarks CIFAR- 10, CIFAR-100, and ImageNet 1k. can be seen that SISOMe achieves for CIFAR-10 top-1 position for near- and far-OOD for CIFAR-10. For CIFAR-100 and ImageNet 1k CTSOMeachiee the highest near-OOD score but shows weaker scores for far- OOD. This can be explained by the additional focus on AL, which is more related to near-OOD. Overall, is apparent. that most methods primarily focused either
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tin cifar10
cifar10
75
50
25
-50
-50
-75
100
-100
-75
-50
-25
50
-75
-50
-25
50
75
(a) Without feature enhancement
(b) KT. Divergence feature enhancement
Figure 11: T-SNE comparison of the latent space for OOD detection with and without KL-Divergence feature enrichment.
InD nOOD 1OOD
InD
InD
nOOD
nO0D
Donsity
0.75
Density
0.75
Density
1OOD
0.50-
0.50
0.25
0.00
0.0
2.5
0.0
1.0
din
dout
(a) Inner class distance
(b) Outer class distance
(c) Distance quotient
Figure 12: Density plots for the inner class distance, outer class distance, and the distance quotient of SISOM CIFAR-10 with near-OOD (nOOD) and far-OOD (FOOD) as defined in OpenOOD.
ncar-OOD far-OOD, creating trade-off between these two metrics. Additionally, the performance of individual methods varies significantly across different datasets. For instance, GEN ranks first in near-OOD for CIFAR-100 but is outperformed by SISOMe in the far-OOD category. Furthermore, for CIFAR-10 and ImageNet 1k, it has not even a top-3 rank for near- or far-OOD. In this context, the results of SISOMe with top-3, including one top-1 position are outstanding, especially in the targeted near-OOD setting. Details on the rank calculations are given in Appendix B.2
A.6 Extended Life Cycle Experiments
In Fig. we evaluated the life cycle performance when deployed for OOD detection as shown in Fig. 3 For near-OOD SISOMe always the best method, while for far-OOD SISOMeshows higher performance in higher cycles. Since near-OOD is closer to AL, a relatively higher performance is preferred in practice. As for Table we used AL chockpoints of SISOMe for the experiments.
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Table 5: far-OOD (f) and near-OOD (u) benchmark for CIFAR-10, CIFAR-100, and ImageNet 1k with Cross-Entropy training. Dataset and official checkpoints according to OpenOOD Yang et al., (2022) bench- mark setting for the respective datasets. Given the number of baselines, for each dataset, we estimated a rank for each category and calculated the joint ranking over all datasets for near-OOD and far-OOD. For individual datasets, the top three ranks are indicated from first to third using bold, double underlined single underlined formatting.
SISOMe
Foxt. Norm
Bank Feat
CombOOD
NNGaide
SISOM
NAC
Con
GEN
MSP
Energy
BoAct
ODIN
KLM
DICE
ASH
SCALE
Datasel
CIFAR-10
91.76
91.40
90.93
90.64
90.34
88.20
88.03
87.58
87.11
85.52
82.87
79.46
79.19
78.34
75.27
82.55
91.13
87.56
94.74
94.50
91.60
92. 96
92.85
91.35
90.73
91.21
90.42
95.59
87.96
75.87
82.68
81.23
78.
86.39
94.65
90.88
CIFAR-100
81.10
79.42
75,90
80.18
75.69
81.31
80.27
80
80. 77
47.87
79.90
61.88
76.56
79.38
78. 20
80.99
78.77
81.25
f
80.16
77.91
86.98
82. 10
79.53
79.68
77.76
79.77
80.39
80.99
79.28
67. 10
76.24
90.01
80.58
81/12
85.87
81.21
NmtgeNet 1k
78.39
77.33
74.43
71.10
76.85
76.02
76.03
77.38
67.57
74.75
50.99
76.64
73.07
78.17
81.36
95.22
73.57
[
89.04
88.01
95.29
90.18
89.76
85, 23
89
..50
93.67
91.13
89.47
53.93
87.60
90.95
95. 74
96.53
90.22 00.24
93.82
Near Rank
11
2
16
17
15
12
Far Rauk
13
2
18
88
86
84
AUROC
AUROC
82
78
80
SI5OMe
SISOMe (ours)
ReAct
RoAct
76
78
Gen
MSP
MSP
Ash
Nac
Nac
6
8
9
10
8
10
Cycles
Cycles
(a)
Near-OOD
(b) Far-OOD
Figure 13: Full life cycle experiments evaluating ncar-OOD and far-OOD AUROC for different active learning checkpoints.
A.7 Time Analysis
Analysis on OOD Detection: Table D outlines the runtime for various OOD delection methods on CIFAR-100. Our method, SISOMe, recorded a total time of 1177.50 + 3.27 seconds, with the evaluation phase comprising 885.66 + 3.31s for the full set evaluation. This runtime is longer than other evaluated methods due to the computationally expensive pairwise distance calculations in SISOMe. However, with our reduced subset to 5%, we can significantly reduce the runtime by almost 400%, which brings SISOMe the same range as other basedd approaches like KNN or NNGuide.
Analysis Active Learning: The per-cycle runtime analysis for AL methods CIFAR-100 is presented in Table 7 Our proposed methods, SISOM and SISOMe, demonstrate runtimes substantially more efficient than the diversity-based Badge method (e.g., 33664 + 6682s in Cycle 0) and are competitive with, or often faster than, Coreset. However, mothods that leverage graph neural networks for dimensionality reduction, like CoreGCN, or individual evaluation strategies without distance calculations, such as LLoss and NAC, naturally exhibit faster per-cycle exccution times due their less complex selection mechanisms.
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Table 6: OOD Runtime Analysis for different methods with CIFAR-100 as the InD dataset. Time is measured on a desktop PC with A6000 and 64GB of RAM. Total time is the sum of setup time and eval time.
SISOMe (ours) 100%
SISOMe (ours) 3%
NAC
KNN
GEN
MSP
ReAct
ODIN
NNGuide
Time (6)
1177 pmu 3:27
290.89
1.19
63.6 1.4
1884
112
238
31.8
13.7
21035
Eval Time (6)
885.66 3.31
251.77 0.74
57.7 = 0.4
166. # 11.
2 0.7
22. 0.7 22.9
22.90.0.
56.1 + 0.1 259.4 =22
Table 7: Active Learning runtime analysis for methods with CIFAR-100 in the high data selection regime. Time is measured on a Kubernetes pod with V100 and 24GB of RAM from a Nvidia DGX machine. *CoreCGN leverages a graph neural network to reduce the dimensionality before calculating the distances, which grands more efficient distance calculations.
Diversity-
Individual Evaluation
SISOM (ours)
STSOMe (ours)
Badge
Corrset
CoreGCN
T.Loss
NAC
Random
Cycle 0
1477
896
126
33664 + 6682
+ 1572
121
7.46
0.19
25.50
0.12 0.01
Cycle 1
1492
865
1084
31019 1: 5752
2740 1: 1753
115
5.81 1 0.05
25.06
0.01 0.00
Cycle 2
1477 782
1117 - 127
34229 + 9662
2886 + 1612
529 111
5.43 + 0.05
25.20
0.12
0.01 = 0.00
Cycle 3
1374 693
1077 - 165
27274 + 3331
2203 866
511
131
5.21 + 0.02
25.63
0.17
0.01 + 0.00
Cycle 4
1432 + 744
1124 + 131
25676 +: 1280
2213 1 889
519 - 114
4.91 + 0.06
25.83 1 0.36
0.01 1 0,00
Cycle 5
1445 + 566
1178 + 91
27286 + 4139
2030 + 900
506 = 114
4.65 + 0.05
25.69 = 0.06
0.01 = 0,00
Cycle 6
1372 + 654
1176 + 93
23590 + 3793
1780 + 741
520 112
4.46 + 0.01
25.88 + 0.07
0.01 + 0.00
Cycle 7
1160 + 410
1150 +: 72
20324 1: 1041
2172 1 1177
535 1 86
4.19 1 0.09
26.22 + 0.13
0.01 + 0.00
Cycle 8
1151 + 397
1121 + 70
19328 284
1704 = 818
510 113
3.75
+ 0.01
26.63 = 0.09
0.01 = 0.00
Cycle 9
1197 + 474
1126 + 28
18532 526
1529 622
500 = 133
3.59 + 0.04
27.02 + 0.14
0.01 = 0.00
B Experimental Details
In this section, we provide experiment details support the reproducibility of results by providing the used parameters.
B.1 Active Learning Experiments
In AL experiments, we used 18 and ResNet34 (He al. 2016 model, with the suggested modifica- tions of Yoo & Kweon (2019) presented in CIFAR benchmark repository (kuangliu 2021), which replaced the kernel of the first convolution with 3 3 kernel. Additionally, we used an SGD optimizer with a learning rate of 0.1 and multistep scheduling at 60, 120, and 160, decreasing the learning rate by factor of 10, which are reported benchmark parameters for CIFAR-100 (weiaicunzai 2022). For SVHN and CIFAR-10 we used a learning rate of 0.025 and a cosine scheduler as suggested by Yehuda et al. (2022) For the construction of the feature space, we used the layers after the 4 blocks of ResNet with sigmoid steepness parameters reported in Table
Table 8: Sigmoid steepness parameters for active learning experiments.
Adaptive Average
Dataset
Pooling Layer
Sequential Layer 3
Sequential Layer 2
Sequential Layer 1
CIFAR-10
50
10
1
0.05
CIFAR-100/SVHN
0.1
0.1
0.1
For the Semi- Super-Suee experiments, we followed the suggestion of Yehuda et al. (2022) and used a model pro-train but used the SIMCLR (Chen et al. 2020) pre-training checkpoint instcad of the SCAN (Gansbeke et al. 2020) approach. While Yehuda et al. (2022) used the weights only for the selection and trained a supervised model from scratch, also initialized the task models with this checkpoint.
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B.2 Out- of--issbbbbuttin Experiments
In the OOD experiments, we report the mean of the three different seeds employed in the standard setting of the OpenOOL Yang et al. 2022) framework with ResNet18 for CIFAR-10 and CIFAR-100, For Imagenet, we use the sole Net50 torchvision checkpoint provided in the standard settings. We utilized the near- and far-OOD assignments suggested by the benchmark listed below. We followed the official tables of OpenOOD's benchmark and reported the mean without the standard deviation. For the CTFAR- R-100 experiment, instead of using the automated favg value to balance between r and E' from Eq. { we set Tavg 0.8 for S1SOMe based hyperparameter study. In the benchmark tables, reported for SISOM the best values matching the best values of the ablation study modifications. Furthermore, parameters (Liu et al.) 2024) for CIFAR-10 and ImageNet. For CIFAR-100, we choose values that minimize Tang- A detailed overview of the sigmoid steepness parameters for the 4 blocks of Net1e and ResNet55
follow the suggested sigmoid steepness
for all experiments is provided in Table g
Table 9: Sigmoid steepness paramcters for OOD Detection experiments.
Adaptive Average Pooling Layer
Dataset
Sequential Layer 3
Sequential Layer 2
Sequential Layer 1
CIFAR-10
100
1000
0.001
0.001
CIFAR-100
0.1
0.1
0.1
ImageNet
3000
300
0.01
OOD dataset assignment according to OpenOOD Yang al. 2022):
CIFAR-10
Near-OOD: CIFAR-100 Krizhevsky et al. 2009), Tiny ImageNet (Le & Yang 2015)
Far-OOD: MNIST (LeCun et al. 1998). SVHN (Netzer et al. 2011), Textures Cimpoi et al, 2014
Places365 López-Ciiuenter Cifuentes et al. 2020
CIFAR-100
Near-OOD: CIFAR-10 (Krizhevsky et al. 2009
Tiny ImageNet (Le & Yang 2015)
Far-OOD: MNIST (LeCun et al. 1998).
SVHN Netzer et al. 2011), Textures
Cimpoi et al. 2014
Places365
Cifuentes et al. 2020
ImageNet
Near-OOD: SSB-hard (aze et al. 2021
NINCO (Bitterwolf et al.
2023
Far-OOD: iNaturalist (Van Horn et al. 2018), Textures (Cimpoi et al. 2014
Openlmage-0 0 |Wang
ct al. 2022
Rank Calculation: Since the performance of the methods differs between near-OOD, far-OOD and the different benchmarks, we use the benchmarks rank for the evaluation. The estimate rank, we add the rank of the method for each benchmark and estimate overall ranking based on the combined rank values
Full Cycle OOD Experiments: For the full cycle OOD experiments, we used three seeds as common of OpenOOD of a CIFAR-10 AL cycle. The three seeds are taken from a SISOMe training and used as checkpoint for all OOD detection methods. Besides providing different checkpoints, we followed the OpenOOD bench- mark procedure for CIFAR-10 with the same data splits. As the checkpoint corresponded to SISOMe and has seen less data, only report our SISOMe.
B.3 Ablation Study
In this section, we highlight the relevant parameters for the ablation study experiments on SISOM. Namely, we examine the Optimal Sigmoid Steepness (OS) and the Reduced Subset Selection (RS) shown in Table 3
htttps://2jysteven gitthu..o//ppeee.
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Table 10: Parameters for the Ablation Study, Coverage Radius for RS and Search Space of Optimal Sigmoid Steepness. Best performing values in bold.
Coverage Radius
Sigmoid Search Values
Datasel
Layer
Adaptive.vg@Pool2211
100, 1000
Sequential-3
1, 10, 1000
CIFAR-10
0.75
Sequential-2
0.001, 0.1, 1
Sequential-1
0.001, 0.1, 1
AdaapiiveAvgPPool22-1
1, 50, 100
Sequential-3
0.1, 10, 100
CIFAR-100
5.0
Sequential-2
0.1, 1
Sequential-1
0.005, 0.1
Adaptive.ArgPool22-1
10, 100, 3000
Sequential-3
1, 10, 300
TmageNett
10.0
Sequential-2
0.1, 1
Sequential-1
0.1, 1
In the experiments conducted with RS, representative subset size of 10% relative to the original training set used across all experiments. Additionally, the specific distance radius used for the class-wise coverage CIFAR-10, 10, CLFAR-100, and ImageNet is provided in Table [0 A non-class-wiee distance radius also used in related approach (Yehuda et al. 2022).. For SISOM + RS without OS, the suggested sigmoid steepness parameters (Luu et al. (2024) emphasized in Appendix B.2 were used. For the OS modification, the search space for the optimal sigmoid parameters is presented in Table 10
The parameters fulfilling the
minimization of rang are highlighted in bold.
B.4 Latent Space Visualization
For the UMAP visualization, we used a SISOM AL checkpoint from the last step for CIFAR-10. For the InD data, we visualized the label and remaining unlabeled samples. For OOD, we followed the categorization of OpenOOD and used Tiny ImageNet (Le & Yang 2015) as the near-OOD dataset and SVHN (Netzer et al. 2011 as for dataset.
25


Reviews:
[{"review_id": "ITZZmovTsf", "reviewer": "Reviewer_vovk", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n- The method brings together two disjoint fields: active learning and OOD detection. These two fields share a similar goal of detecting data different from the data seen during training, but doing it for different reasons. Active learning postulates that data different from the seen one is more useful for future model training. On the other hand, OOD detection wants to filter out the data outside of the model's distribution. \n- The method presented in the paper is the first one that operates in both tasks and achieves competitive results.\n- The proposed method is simple, and the design choices are well motivated.\n- The paper is clearly written and easy to follow.\n\n**Weaknesses**\n- Evaluation of active learning experiments could be improved.\n\t- Active learning is evaluated only on tiny datasets such as CIFAR and SVHN, while recently, many active learning methods [1, 2, 3] are moving their evaluation to more realistic datasets such as ImageNet. It would be beneficial to show that the proposed method performs well in this realistic setup.\n\t- The method is evaluated only using the ResNet18 backbone. Although this is the most common backbone in active learning, it would be good to show the performance of the proposed method with larger backbones, e.g. ResNet50, as used in recent active learning methods [1].\n- The paper is missing a comparison with some state-of-the-art OOD detection methods.\n\t- Some of these are SCALE [4], CombOOD [5], and NNGuide [6].\n\t- Additionally, the performance of some of the competitors in near-OOD is different than what is reported on the official OpenOOD benchmark website, e.g. NAC for ImageNet (71.73 vs 74.43).\n- The paper claims that OpenSet Active Learning methods tackle active learning selection and OOD detection with separate modules, which is not true for all methods. Two recent methods [3, 7] handle the OOD detection and classification using the same model. OOD detection simply uses an additional class in the classifier that handles all other classes.\n\n*References*\n\n[1] Guy Hacohen, Avihu Dekel, Daphna Weinshall, \"Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets\", In ICML, 2022\n\n[2] Dongmin Park, Yooju Shin, Jihwan Bang, Youngjun Lee, Hwanjun Song, Jae-Gil Lee, \"Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning\", In NeurIPS, 2022\n\n[3] Vladan Stojnić, Zakaria Laskar, Giorgos Tolias, \"Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning\", In WACV, 2024\n\n[4] Kai Xu, Rongyu Chen, Gianni Franchi, Angela Yao, \"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement\", In ICLR, 2024\n\n[5] Magesh Rajasekaran, Md Saiful Islam Sajol, Frej Berglind, Supratik Mukhopadhyay, Kamalika Das, \"COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification\", In Proceedings of the International Conference on Data Mining, 2024\n\n[6] Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh, \"Nearest Neighbor Guidance for Out-of-Distribution Detection\", In ICCV, 2023\n\n[7] Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty, Rishabh Iyer, \"SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios\", In NeurIPS, 2021", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a method for active learning and OOD detection. The method calculates the distance ratio of the test sample and the closest labeled samples coming from the same/different class as the predicted class of the test sample. Active learning selection is then performed by selecting samples with the highest distance ratio. On the other hand, the OOD detection is performed by mapping the distance ratio to the $[0,1]$ range and then thresholding. The method is evaluated on three benchmarks for active learning and three benchmarks for OOD detection, and the proposed method performs well across all of them.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "*Critical*\n- The paper should include the comparisons with the missing state-of-the-art OOD detection methods and fix the discrepancies with the official results from the OpenOOD benchmark, as mentioned in the weaknesses.\n- The paper should fix the claims about all OpenSet Active Learning methods using separate modules for active learning and OOD detection, and include the discussion of existing works that use a single module.\n- The paper should include the running time of the proposed method and compare it with the existing methods for active learning and OOD detection.\n\n*To strengthen the work*\n- The paper should evaluate the proposed active learning method on larger benchmarks, e.g., ImageNet, and using larger backbones, e.g., ResNet50.\n- The paper should include the absolute performance in the active learning experiments shown in Figure 6 and Figure 7, as showing only the difference to random selection does not show whether the trained models are performing well.", "broader_impact_concerns": "The work does not have any ethical concerns that would need to be addressed in a broader impact statement.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "KXr4O9N5ge", "reviewer": "Reviewer_F3xM", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Below, I list the *strengths* and *weaknesses* according to their importance, starting with the most important one and ending with the least important one. I will reconsider my perspective on these issues if I have misunderstood or overlooked any aspect. Looking forward to a constructive discussion!\n\n**Strengths:**\n- SISOM addresses a critical yet underexplored problem of label-efficiently training an accurate classification model, simultaneously enabling robust OOD detection.\n- The proposed distance ratio has an intuitive motivation and is easy to compute. Moreover, the average ratio of the labeled instances allows for defining a criterion for estimating the feature space's expressiveness, which balances the model uncertainty with the inner-to-outer class ratio. The idea is to trust the model's estimated uncertainties more if the feature space's expressiveness is low.\n- SISOM is a modular system extracting expressive instance representations by combining established research concepts, such as concatenating multiple layers' latent spaces and gradient-based weighting of individual layers, as building blocks. The authors demonstrate the benefit of the particular building blocks in an ablation study.\n- The experimental details and the provided code allow other researchers to reproduce the experimental results.\n\n**Weaknesses:**\n- While I acknowledge the effort of performing an evaluation study covering AL and OOD tasks, there is not \"extensive\" evidence to reliably assess the performance of SISOM in AL tasks. For example, the current AL setup covers only CIFAR-10, CIFAR-100, and SVHN as three relatively simple benchmark datasets. Moreover, there is no information on how the acquisition size per cycle has been determined. Accordingly, the paper would benefit from following more of the recommendations of established evaluation protocols [1, 2].\n- As noted by the authors, the computational complexity can be a bottleneck in AL. Subset selection is a standard procedure to address this issue, and the authors have also studied its impact on SISOM. However, there is no further comparison regarding the selection times of SISOM to those of its competitors. For example, SISOM requires the evaluation of gradients, distance computations for latent feature spaces covering multiple neural network layers, and optimizing the $\\alpha$ to maximize the feature space separability.\n- Certain design choices are not fully explained. For example, the instance selection is performed via the top-$k$ selection scheme in Eq. (8). However, there is no guarantee that this batch contains diverse instances because many similar instances could lie between clusters in the latent space. So it is unclear is whether there could be issues with redundant instances within a selected batch. \n\n**References:**\n- [1] Lüth, Carsten, et al. \"Navigating the pitfalls of active learning evaluation: A systematic framework for meaningful performance assessment.\" Advances in Neural Information Processing Systems (2023).\n- [2] Werner, Thorben, et al. \"A Cross-Domain Benchmark for Active Learning.\" Advances in Neural Information Processing Systems (2024).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors present **Simultaneous Informative Sampling and Outlier Mining (SISOM)** as an approach to unify *active learning* (AL) and *out-of-distribution detection* (OOD) objectives. Its core idea is to adopt existing techniques, enhancing the latent feature space's expressiveness, as the basis for computing a inner-to-outer class distance ratio. In an AL setting, this novel ratio guides the selection of instances between clusters within the latent feature space for labeling. At the same time, it enables the separation of *in-distribution* (ID) and OOD data. Due to the two objectives, the authors perform a twofold evaluation study. On the one hand, they evaluate the AL component of SISOM compared to other (well-known or state-of-the-art) AL strategies across image datasets for classification. On the other hand, they assess the OOD detection component of SISOM by following an established OOD detection benchmark.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "I would appreciate it if the authors could address the abovementioned *weaknesses* and clarify the following *minor remarks* and *questions*. \n\n**Minor Remarks:**\n- The mathematical notation is, from time to time, challenging to follow. For example, in Eq. (5) and (6), the inner-class and outer-class distances are introduced as plain variables. At the same time, they are used as functions in Eq. (10). Another example would be Eq. (3) and (4), where the meaning of $\\mathbf{z}_i$ (likely $h_i(\\mathbf{x})$) and $\\odot$ (likely Hadamard product) is not fully disclosed.\n- A few paragraphs would benefit from more references. For example,  the first paragraph in Section 1 makes factual statements about mobile robotic perception without citing works for confirmation.\n- There are minor typos, e.g., \"Sigmoid Stepness\" in Fig. 3. \n\n**Questions:**\n- Is there a reason for reporting only OOD results corresponding to one AL checkpoint in Table 2 instead of curves indicating the development of OOD detection performance over the number of AL cycles?\n- Does the relative accuracy in the learning curve plots refer to relative improvements over random sampling? For example, if one AL strategy achieves 10.1% accuracy and random sampling only 10% accuracy, does this correspond to a relative improvement of 1%? \n- Do you have a potential explanation for why your results in Fig. 7 indicate rather bad performance for TypiClust, while other works [1] have confirmed high performance for CIFAR-10? Is it because the amount of labeled data corresponds to a high-budget scenario as shown in [2]?\n- Why do you evaluate AL strategies such as BADGE not in the semi-supervised AL setup?\n\n**References:**\n- [1] Bae, Wonho, et al. \"Generalized Coverage for More Robust Low-Budget Active Learning.\" European Conference on Computer Vision (2024).\n- [2] Bae, Wonho et al. \"Uncertainty Herding: One Active Learning Method for All Label Budgets.\" International Conference on Learning Representations (2025).", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "W60RA1697J", "reviewer": "Reviewer_ey25", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper is clearly written, the images are beautiful, and the experiments seem to prove the effectiveness of SISOM. I must admit that I am not an expert on Active Learning, so I cannot comment on the technical aspects of the work. What I can say is that it definitely is of interest for the TMLR readers, and that the problem studied by the authors is very interesting. As such, I recommend the paper is accepted after minor revision.\n\nThe weakness that I was able to detect (not really a weakness of course, but rather a missed related work), is highlighted in the following section.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors' work explores the connection between Active Learning (AL) and Out of Distribution (OOD) detection, introducing a unified approach for both tasks that leverages their mutual strengths.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Recently, in https://arxiv.org/abs/2308.14815, accepted at HSCC 2025 (https://hscc.acm.org/2025/accepted-papers/), the authors discuss the first ever application of techniques from the imprecise probability literature to AL, precisely to solve problems similar to the ones that the authors are interested in (AL + OOD). It would be nice to see a small discussion on how the two papers relate.\n\nMore in general, we suggest the authors to dig --- for their future endeavors --- in the Imprecise Probabilistic Machine Learning field (see contributions by Hüllermeier, Sale, Caprio, Destercke, Cuzzolin, De Campos, Zaffalon, Denoeux, Williamson, Muandet, Benavoli, Cella, Gong, and Cozman), whose tools are extremely helpful to approach and attack problems similar to the ones presented in this paper.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)