Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
Authors: Shoaib Ahmed Siddiqui
Nitarshan Rajkumar
Tegan Maharaj
David Krueger
Sara Hooker
Venue: ICLR 2023 notable top 25% (2023)
Keywords: Metadata archaeology
Learning curves
Loss trajectory
Data auditing

Abstract: Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.

Published as a conference paper at ICLR 2023
METADATA ARCHAEOLOGY: UNEARTHING DATA
SUBSETS BY LEVERAGING TRAINING DYNAMICS
Shoaib Ahmed Siddiqui University of Cambridge msas30cam.. ac.uk uk
Nitarshan Rajkumar University of Cambridge nr500@cam.ac ac..aamaac..... uk
Tegan Maharaj University of Toronto tegan wwharaahaaaaaaaaaaaa jut.orrnooaooooooocccoooo
David Krueger
Sara Hooker Cohere for AI
Universily of Cambridge dsk300cam. ac uk
sarahooker@cchere.com com
ABSTRACT
Modern machine learning research relies on relatively few carefully curated datasots. Even in these datasets, and typically 'untidy' or raw data, prac- titioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: instead focus on providing a unified and efficient. framework for Meladala Archaeology uncovering and inferring metadata of examples in dataset. We curate dif- ferent subsets of data that might exist in dataset (e.g. mislabeled, atypical, examples) using simple transformations, and lever- age differences in learning dynamics between these probe suites to infer metadata of interest. Our mothod is on par with far more sophisticatod mit- igation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
1 INTRODUCTION
Modern machine learning is characterized by ever-larger datasets and models. The expanding scale has produced impressive progress (Wei et al. 2022 Kaplan et al. 2020 Roberts et al. 2020) yet presents both optimization and auditing challenges. Real- worldwordd dataset collection techniques often result in significant label noise (Vasudevan et al. 2022). and can present significant numbers of redundant, corrupted, or duplicate inpuls (Carlini el al. 2022). Scaling the size of our datasets makes detailed human analysis and auditing labor-intensive, and often simply infeasible. These realities motivate consideration of how to efficiently characterize different aspects of the data distribution.
Prior work has developed a rough taxonomy of data properties, or metadata which different examples might exhibit, including but not limited to: noisy (Wu et al. 2020 Yi and Wu
2019
Thulasidasan et al. 2019a b). atypical (Hooker et al. 2020 Buolamwini and Gebru
2018
Hashimoto et al.] 2018 Slowik and Bottou 2021). challenging (Ahia et al.) 2021
Baldock et al. 2021 Paul el. al. |021 et al. 2021). protolypical or core subsel
selection (Paul et al. 2021 Sennr and Savarese 20088 Shim
2021: Huggins et al.
2017
Sorscher et al.] 2022 and puttoo-diistrbuutio (Hendrycks et al. 2019 LeBrun et al. 2022
While important progress has been made on some these metadata categories individually,
those categories are typically addressed in isolation reflecting an overly strong assumption
that only onc, known issuc is at play in a given datasct.
For example, considerable work has focused on the issue of label noise. A simple yet widely- used approach to mitigate label noise is to remove the impacted data examples (Pleiss et al. 2020). However, it has been shown that it is challenging to distinguish difficult examples

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Digital Watch
Plastic Bag
Toilet Tissue
Typical
Corrupted
Atypical
Random
Output
Figure 1: Examples surfaced through the use of MAP-D on ImageNet train set. Column title is the ground truth class, row title is the metadata category assigned by MAP-D. MAP-D performs metadata archaeology by curating a probe set and then probing for similar examples based on training dynamics. This approach can bring to light biases, mislabelled examples, and other dataset issues.
from noisy ones, which often leads to useful data being thrown away when both noisy and atypical examples are present (Wang et al. 2018 Talukdar et al. 2021h.
Meanwhile, lass- prioritization (Jiang et al. 2019 Katharopoulos and Fleuret 2018 techniques essentially do the opposite these techniques upweight high loss examples, assuming these examples are challenging yet lcarnable. Those methods have been shown to quickly degrade in performance in the presence of even small amounts of noise since upweighting noisy samples hurts generalization (Hu al.) 2021 Paul al. 2021).
The underlying issue with such approaches is the assumption of single, known type of data issue. Interventions are often structured to identify examples as simple vs. challenging, clean vs. noisy, typical vs. atypical, in-distribution vs. out-of-distribution etc. However, large scale datasets may present subsets with many different properties. In these settings, understanding the interactions between an intervention and many different subsets of interest can help prevent points of failure. Moreover, relaxing the notion that all these properties are treated independently allows us to capture realistic scenarios where multiple metadata annotations can apply to the same datapoint. For example, challenging example may be so because it is alypical.
In this work, we are interested in moving away from a siloed treatment of different data properties. We use the term Metadata Archaeology to describe the problem of inferring metadata across a more complete data taxonomy. Our approach, which we term Metadata Archaeology via Probe Dynamics (MAP-D), leverages distinct differences in training dynamics for different curated subsets to enable specialized treatment and effective labelling of different metadata categories. Our methods of constructing these probes are general enough that the same probe category can be crafted elficiently for many different datasets with limited domain-specific knowledge.
We present consistent results across six image classification datascts, CIFAR-10/CIFAR- (Krizhevsky et al. 2009
ImageNet (Deng et al. 2009). Waterbirds (Sagawa et al. 2020 ClothingIM (Xiao et al. 2015) and two models from the ResNet
CelebA [Lu et al. 2015
family He et al. 20163.
Our simple approach is competitive with far more complex mitigation
techniques designed to only treat one type of metadata in isolation. We summarize our contributions as:
We propose Metadata Archacology, unifying and general framework for uncov- ering latent metadata categories.
We introduce and validate the approach of Metadata Archacology via Probe Dynamics (MAP-D): leveraging the training dynamics of curated data subsets called probe suites to infer other examples' metadata.
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We show how MAP-D could be leveraged to andit large-scale datasets or debug model training, with negligible added cost see Figure This is contrast to prior work which presents siloed treatment of different data properties.
We use MAP-D to identify and correct mislabeled examples in a dataset. Despite its simplicity, MAP-D ou-par with far more sophisticated methods, while enabling natural extension to an arbitrary number of modes.
Finally, we show how to use MAP-D to identity minority group samples, or surface examples for data-efficient prioritized training.
2 METADATA ARCHABOLOGY VIA PROBE DYNAMICS (MAP-D)
Metadata is data about data, for instance specifying when, where, or how an example was collected. This could include the provenance of the data, or information about its quality (e.g. indicating that has been corrupted by some form of noise). An imporlant distinguishing characteristic of metadata is that it can be relational, explaining how an example compares to others. For instance, whether an example is typical or atypical, belongs to a minority class, is outtrf-distributtion (OOD), are all dependent on the entire data distribution.
The problem of metadata archaeology the inference of metadata m C . given dataset D. While methods for inferring might also be useful for semi-supervised labelling or more traditional feature enginecring, and vice versa, it is the relational nature of metadata that makes this problem unique and often computationally expensive.
2.1 METHODOLOGY
Metadata Archaeology via Probe Dynamics (MAP-D), leverages differences the statistics of learning curves across metadata features to infer the metadata of previously unseen examples.
We consider model which learns function fo X 3/ with trainable weights 6. Given the training dataset D, fe optimizes a set of weights 8" by minimizing an objective function L with loss / for each example. We assume that the learner has access to two types of samples for training. First is a training set D:
D (11, (IN. UN)} xxxy,
(1)
where X' represents the data space and y the set of outcomes associated with the respective instances. Examples in the data space are also assumed to have associated, but unobserved, metadata C. M. Secondly, subset of j samples (j S N) associated with metadata m C. M, i.e.:
assume the learner to also have access to small curated
Dm {( v1. m1)...))... ... (ry, My. my)) X
(2)
We refer to these curated subsets probe suites. A key criteria is for our method to require very few annotated probe examples (j & N). In this work, we focus on probe suits which can be constructed algorithmically, as human annotations of metadata require costly human effort to maintain.
2.1.1 ASSIGNING METADATA FEATURES TO UNSEEN EXAMPLES
MAP-D works by comparing the performance of given example to the learning curves typical of a given probe type. Our approach is motivated by the observation that different types of examples often exhibit very different learning dynamics the course of training (sce Figure 3h. In an empirical risk minimization sctting, we minimize the average training loss across all training points.
1,0) Σ e (2., 0:;
However, performance given subset will differ from the average ervor. Specifically, we firstly evaluate the learning curves of individual examples:
sf = (f(2r, yi; 0z) (xx,, ye, 02), ((x,, 0t) (xi, ve) (D)
(3)
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Black bear
Dishwasher
School bus
Mud turtle
Jeep
Loafer
(a) Typical
(b) Atypical
(c) Corrupted
(d) Rand Label
(e) OOD
(I) Rand Input
Figure 2: An illustration of samples from our curated probes. Creating our probe suites requires no human annotation. We curate different subsets of data that might exist in a dataset including (a) typical, (b) atypical, (c) corrupted, (d) mislabeled, (e) out-of- distribution, and (f) random input examples, using simple transformations or existing scoring mechanisms.
where sf denotes the learning curve for the ith training example, and is the current epocl An important property of the loss function is that it naturally integrates information regarding the whole data distribution via training. This is essential for metadata inference which is relational in nature. We then track the per-example performance probes g for each metadata category m E (/) and refer to each probe as g(m).
(m)) == (f( U5: 01), (ay, My .e., EZz Us: (Izz Uj) E Dm)
(4)
where (m) denotes the learning curve computed on the jth example chosen from a given probe category We use D, shorthand to refer to the set of all these trajectories for the different probe categories along with the category identity.
Dg:= = (g)) m.)..,,) (B'm.,,,,, m1), (gi (m2), (gj))...,,,,,,,,,,,) (B/m)// (mpm)) m(ni) (5)
where |mel refers the number of examples belonging to the metadata category me-
We assign metadata features to unscen data point by looking up the example's nearest neighbour from Dg, using the Euclidean distance. In general, assignment of probe type could be done via any classification algorithm. However, in this work we use k-NN (k-Nearest Neighbours) for its simplicity, interpretability and the ability to compute the probability of multiple different metadata features.
p(m st) =
Σ
Am=
(g.a) E NN.s
where p(m st) is the probability assigned to probe category based the & nearest noighbors for learning curve of the ith training example from the dataset, and NN(sf. D k) represents the top-k nearest neighbors for st from Dg (probe trajectory dataset) based on Euclidean distance between the loss trajectories for all the probe examples and the given training example. We fix k= all our experiments.
This distribution probes (i.e metadata features) may be of primary interest, but sometimes also interested in seeing which metadata feature a given example most strongly corresponds to; in this case, compute the argmax:
mf = arg max p(m |
mEM
where m denotes the assignment of the ith example to particular probe category.
We include the probe examples in the training set unless specified otherwise; excluding them training can result a drift in trajectories, and including them allows tracking of training dynamics.
A coarser finer resolution for the learning curves could also he used, e.g. every steps or epochs. All experiments in this work differences computed at the end of the epoch.
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Alylicll Compptdd ToA
100
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(a) Probe Accuracy
(b) Percent First-Learned (c) Percent Consistently-Leande
Figure 3: Probe categorics are distinguishable via learning dynamics of a ResNet-50 trained on ImageNet, validating the approach of MAP-D. For each of the probe categories and at each epoch, we plot (a) each probe's average accuracy; (b) the cumulative fraction of examples once predicted correctly by the nth epoch; and (c) the fraction that remain predicted correctly on all subsequent epochs.
2.2 PROBE SUITE CURATION
While probe suites can be constructed using human annotations, this can be very expensive to annotate (Andrus et al., 2021 Veale and Binns 2017). In many situations where auditing is desirable (e.g. toxic or unsale content screening). ). extensive human labour is undesirable or even unethical (Steiger et al. 2021 Shinuel et al. 2021). Hence, in this work, we focus probes that can be computationally constructed for arbitrary datasets largely by using simple transformations and little lomain-specific knowledge. We emphasize that our probe suite is not meant to be exhaustive, but to provide enough variety in metadata features to demonstrate the merits of metadata archaeology.
We visualize these probes in Figure 2 and describe below:
1. Typical We quantify typicality by thresholding samples with the top consistency scores from Jiang et al. (2020) across all datasets. The consistency score measure of expected classification performance on held-out instance given training sets of varying size sampled from the training distribution.
2. Atypical Similarly, atypicality quantified as samples with the lowest consistency scores from Jiang et al. (2020).
3. Random Labels Examples in this probe have their labels replaced with uniform random labels, modelling label noise.
4. Random Inputs & Labels These noisy probes are comprised of uniform U(0, 1) noise sampled independently for every dimension of the input. We also randomly assign labels to these samples.
5. Corrupted Inputs Corrupted examples constructed by adding Gaussian noise with mean and 0.1 standard deviation for CIFAR-10/100 and 0.25 standard deviation for ImageNet. These values were chosen to make the inputs noisy as possible while still being (mostly) recognizable to humans.
We curate 250 training examples for each probe category. For categories other than Typi- cal/ Atypial,,,,, we sample examples at random and then apply the corresponding transforma- tions. We also curate 250 test examples for each probe category to evaluate the accuracy of our nearest neighbor assignment of metadata to unseen data points, where know the true underlying metadata.
Training Details and Architectures: We list all the training details and architectures in Appendix|a [A] All our experiments are based on ResNet-50 (He et al. 2016). except label correction experiments which are based on ResNet- 18 following Arazo et al. (2019
3 EXPERIMENTS AND DISCUSSION
In the following sections, we perform experiments across 6 datasets: CIFAR-10/100, Ima- geNet, Waterbirds, CelebA, and Clothing M. For details regarding the experimental setup,
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0.75
915
184
000
Tylical
A11
9.29
00
600
33
0.1
0.22
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9.26
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(a) Loss trajectories
(b) Confusion matrix
Figure 4: Sanity check showing performance of MAP-D on the probe suite test set with four main probe calegories using ResNet-50 on ImageNet, where we know the ground-truth metadata. (a) Solid line shows the mean learning curve while translucent lines randomly sampled 250 individual trajectories for each probe category. Again, the separation of different probes is evident both in the dynamics over the course of training. (b) show confusion matrices between the true vs. predicted metadata features, demonstrating strong performance of the probes (scc Fig. 12 for depiction of performance all probe categorics).
see Appendix A We first evaluate convergence dynamics of different probe suites (Sec- tion 3.1, validating the approach of MAP-D. We then qualitatively demonstrate the ability to audit datasets using MAP-D (Section 3.2) and evaluate performance on a variety of reamstream lasks: noise correction (Section 3.3) prioritizing points for training (Section 3.4. and identifying sinority-group samples (Section 3.5
3.1 PROBE SUITE CONVERGENCE DYNAMICS
In Figure 3 we present the training dynamics on the probe suites given a ResNett-50 model on ImageNet. For all datasets, we observe that probe suites have distinct learning convergence trajectories, demonstrating the efficacy of leveraging differences in training dynamics for the identification of probe categories. We plot average 1) Probe Accuracy over the course of training, 2) the Percent First-Learned i.e. the percentage of samples which have been correctly classified once (even if that sample was be later forgotten) over the course of training, and 3) the Percent Consistently-Learnel iee. the percentage of samples which have been learned and will not be forgotten for the rest of training.
We observe consistent results across all dimensions. Across datasets, the Typical probe has the fastest rate of learning, whereas the Random Outputs probe has the slowest. When looking at Percent Lerrmee in Figure 3. we see a very clear natural sorting by the difficulty of different probes, where natural examples are learned earlier compared to corrupted examples with synthetic noise. Examples with random outputs are the hardest for the model.
We also observe that probe ranking terms of both Percent First-Learned and Percent Consistendly-Leameed is stable across training, indicating that model dynamics can be leveraged consistently as a stable signal to distinguish between different subsets of the distribution al any point in training. These results motivate our use of learning curves as signal to infer unseen metadata.
3.2 AUDITING DATASETS
key motivation of our work that the large size modern datasets means only small fraction of datapoints can be economically inspected by humans. In safety-critical or
otherwise sensitive domains such as healthcare diagnostics (Xie et al. 2019 Gruetzemacher
et al. 2018 Badgeley et al. 2019 Oakdeen-Rayner et al.) 2019). self-driving cars (NHTSA
2017). hiring @Dstinn 2018 Harwell 2019). D and many others, providing tools for domain experts to audit models is great importance ensure scalable oversight.

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Figure Comparison of different noise correction methods under the presence label noise. Mean and standard deviation reported over 3 random runs. MAP-D D is competitive with most other methods, many of which are particularly targeted towards this problem.
We apply MAP-D to infer the metadata features of the underlying dataset. In Fig. [ we visualize class specific examples surfaced by MAP-D on the ImageNet train set. Our visualization shows that MAP-D helps to disambiguate effectively between different types of examples and can be used narrow down the set of datapoints to prioritize for inspection. We observe clear semantic differences between the sets. In Fig. [ we observe that examples surfaced as Typical are mostly well-centered images with a typical color scheme, where the only object in the image is the object of interest. Examples surfaced as Atypical present the object in unusual settings or vantage points, or feature differences in color scheme from the typical variants. We observe examples that would be hard for human to classify using the Random Output probe category. For example, we see incorrectly labeled images a digital watch, images where the labeled object is hardly visible, artistic and ambiguous images, and multi-object examples where several different labels may be appropriate. We visualize more examples from the Random Output probe category in Fig.
As a sanity check, we also evaluate the performance of MAP-D on the held-out probe test set in Fig. [ where we know the true underlying metadata used to curate that example. In particular, we compute performance on the four probes which are most easily separable via learning curves, and find that model was able to achieve high detection performance (~ 81.9% accuracy). Results with all probe categories are presented in appendix F The primary aim here is to demonstrate that MAP-D sufficiently capable in detecting the defined probe categories in the dataset.
3.3 LABEL NOISE CORRECTION
Here apply MAP-D to detect and correct label noise, data quality issue that has been heavily studied in prior works (Zhang et al. 2017 Arazo et al. 2019 Arpit et al. 2017]
We benchmark against series of different baselines under the assumption of uniform label noise (Arazo et al. 2019 Zhang et al. 2017 Patrini et al. 2017 Reed et al. 2014). some of which are specifically developed
deal with label noise. We emphasize that our aim is not to develop specialized technique for dealing with label noise, but to showcase that MAP-D, a general solution for metadata archacology, also performs well on specializod tasks such as label correction.
To distinguish between clean and noisy samples using MAP-D, we add an additional random sample probe curated via random sample from the (unmodified) underlying data, as proxy for clean data. For this comparison, we follow the same experimental protocol as Arazo et al. (2019) where all the methods benchmark against evaluated.
Concretely, for any label correction scheme, the actual label used for training is a convex combination of the original label and the model's prediction based on the probability of the sample being either clean or noisy. Considering one-hot vectors, the correct label can be represented as: Vi p(clean s y1 + p(noisy st) ú
(8)

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where yi represents the corrected label used to train the model, ys represents the label present in the dataset weighted by the probability of the sample being clean p(clean st), and ds represents the model's prediction (a one-hot vector computed via argmax rather than predicted probabilities) weighted by the probability of the sample being noisy p(noisy st)- Since we are only considering two classes, p(clean st) = p(noisy st). We employ the online MAP-D trajectory scheme in this case, where the learning curve is computed given all prior epochs completed as of that point. We use the actual probability estimate returned by MAP-D. We highlight the utility of these probability estimates by comparing against the binary prediction bascline in appendix
C
Despite the relative simplicity and generality of MAP-D, it generally performs as well as highly-engineered methods developed specifically for this task. Our results are presented in Fig. 5 Specifically, at extremely high levels of noise, MAP-D performs significantly better on both CTFAR-10 and CIFAR-100 in comparison to Arazo et al. (2019) (CIFAR-10: 47% ~ 59%; CIFAR-100:
6.5% vs ~ 16.5%).
We also show that MAP-D is robust against changes in the training setup, while Arazo et al. Arazo et al.
2019 struggles in those cases in appendix D
3.4 PRIORITIZED TRAINING
Prioritized training refers to selection of most useful points for training in an online fashion with the aim of speeding up the training process. We consider the online batch selection scenario presented in Mindermann et al. (2022), where we only train on a selected 10% of the examples in each minibatch. Simple baselines for this task include selecting points with high loss or uniform random selection. not yet learned (i.e consistently correctly classified), but this can also select for mislabeled examples, which are common in large web-scraped datasets such as ClothingIM (Xiao et al., 2015). As noted by Mindermann et al. (2022), we need to find points which are useful to learn. Applying MAP-D in this context allows us to leverage training dynamics to identify such examples- we look for examples that are not alrcady learned, but which still have training dynamics that resemble clean data:
can be helpful to prioritize examples which are
training score = (clean score + (1. correct class confidence)) / 2. (9)
where clean score is the probability of an example being clean (vs. noisy) according to the k-NN classifier described in Section 2.1.1 An example can achieve a maximun score of 1 under this metric when MAP-D predicts the example is clean, but the model assigns 0 probability to the correct label. Following Mindermann et al. (2022), we select 32 examples from each minibatch of 320. For (class-)ba bblanced sampling, we also ensure that we always select at least examples from each of the 14 possible classes, which significantly improves performance. Figure 6 shows the effectiveness of this approach vs. these baselines; we achieve 10x stepwise speedup over uniform random selection of examples.
We also report the original results from Mindermanu et al. (2022) for reference which uses a different reporting interval. Mindermann al. (2022) requires pretraining a separate model, and uses the prediction of that model to decide which points to prioritize for training. Our method on the other hand uses an online MAP-D trajectory scheme to decide whether an example is clean or noisy" It is important to note that using balanced sampling with RHO Loss is likely to also improve performance for Mindermann et al. (2022).
3.5 DETECTION OF MINORITY GROUP SAMPLES
Minimizing average-case error often hurts performance on minority sub-groups that might be present in a dataset (Sagawa al. 2019 2020 Liu al. 2021). For instance, models might learn to rely on spurious features that are only predictive for majority groups. Identifying minority-grouu samples can help detect and correct such issucs, improving model fairness.
Previons works identify minority examples as those that are not already fit after some number of training epochs, and retrain from scratch with those examples upweighted (Liu et al.
We append the loss values of all examples in the batch to their learning curves before computing the assignments in order ensure that examples can be correctly assigned even the first epoch.
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2021 Zhang al. 2022). The number of epochs is treated hyperparameter; tuning it requires running the training process twice (without and then with upweighting) and evaluating held-out noown-to-be-minoe
examples. Instead of relying on the inductive bias that minority examples will be harder to fit, we apply MAP-D to find examples that match minority examples' training dynamics, and find this is much more effective method of identifying minority examples, see Figure 7 This avoids the costly hyperparameter tuning required by previous methods. Instead of of just using 250 examples per probe category, we use the complete validation set in order to enable fair comparison with JTT (Liu et al. 2021h. Furthermore, these examples are not included as part of the training set in order to match the statistics of examples at Lest lime.
4 RELATED WORK
We divide the related work into two major categories, starting from metadata inference which the primary goal of our work, followed by potential interventions based on the discovered metadata. We provide a more holistic discussion of related work in Appendix F
4.1 METADATA INFERENCE
Our work primarily relates to metadata inference. We consider metadata which is relational in nature. Individual efforts have been targeted towards different metadata properties in
isolation, where an example only ranked along one axis. Examples of such metadata is
mislabeled examples (Arazo et al. 2019). typical atypical examples (Brown et al. 2020 Jiang
ct al. 2020), difficult examples [Agarwal et al. 2021). minority- examples LLu et al.
2021 Sagawa et al., 2020 2019 Zhang el al. 2022 points worth training (Mindermann
al.
2022p. or domain identity in & domain generalization setting (Creager et. al.) 2022]. MAP-D
general method which enables metadata inference for different metadata categories in a consolidated framework leveraging the training dynamics of the network.
4.2 METADATA-SPRCIIFC INTERVENTIONS
Once these metadata categories have been identified, different metadata specific interven- tions can be performed. Example of such interventions could be: correcting mislabeled examples present. in dataset Arazo et al.} 2019). using them only in self-supervieed training objective (Li et al. 2020a). : or even completely ignoring them during training (Wang al. 2018 Talukdar et al. 2021 upweighting or balancing training on atypical, or avoid memorization of noisy labels (Brown et al. 2020), upweighting minority-group samples to
promote model fairness @Liu et al., 2021 Sagawa al., 2020: 2019 Zhang et al. 2022), selectively training on the most important points in batch (Mindermann et al. 2022 or perform group distributionally robust optimization using domain identities (Sagawa et al.] 2019). We show that MAP-D can also be used lo perform specific interventions once the metadata inference phase is completed.
CONCLUSION
We introduce the problem of Meladata Archaeology as the task of surfacing and inferring metadata of different examples in a dataset, noting that the relational qualities of metadata are of special interest (as compared to ordinary data feat ures) for auditing, fairness, and many other applications. Metadata archacology provides a unified framework for addressing multiple such data quality issues in large-scale datasets. We also propose a simple approach to this problem, Metadata Archaeology via Probe Dynamics (MAP-D), based on the assumption that examples with similar learning dynamics represent the same metadata. We show that MAP-D is successful in identifying appropriate metadata features for data examples, even with no human labelling, making it competitive approach for variety of downstream tasks and datascts and a useful tool for anditing large-scale datasets. MAP-D can fail in scenarios where the training trajectories tailored for the task. We provide a detailed discussion of the limitations of our approach and future work in appendix G
not sufficienlly distinct, the probe suite is not correctly
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ACKNOWLEDGEMENTS
The authors would like to thank the SDS department at DFKI Kaiserslautern for their support with computing resources.
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Figure 6: Results for score-based prioritization with MAP-D (pink, lavender) compared against simple baselines. Mean and standard deviation computed over 3 random runs. The correct class score baselines (purple, orange) both select examples with the highest loss, which lead to poor performance due to label noise. Uniform selection baselines (blue, green) perform quite well, but take longer to train; out method achieves almost a 10x stepwise training speedup. RHO Loss* baseline (grey)) plots original results reported in Mindermann itt al. (2022) while all other results use our implementation. While use a different reporting interval, results remain comparable. The two methods are similar in training speed, but MAP-D achieves higher accuracy.
A EXPERIMENTAL DETAILS
In all experiments, we use variants the ResNet architecture and leverage standard image classification datasets CIFAR-10/100 and ImageNet. We train with SGD using standard hyperparameter settings: learning rate 0.1, momentum 0.9, weight-decay 0.0005, and a cosine learning rate docay. We achieve top-1 accuracies of 93.68% on CIFAR-10, 72.80% on CIFAR-100, and 73.94% on ImageNet.
CIFAR-10/100 To account for the smaller image size in this dataset, we follow standard practice and modify the models input layer have stride and filter size We use a batch-size of 128 and train for 150 epochs. We use random horizontal flips and take random crop of size 32 x 32 after padding the image using reflection padding with a padding size of 4 (He et al. 2016). For label noise correction experiments,
follow the experimental protocol of Arazo et al (2019) with ResNet-18 where we train the model for 300 epochs with SGD and initial learning rate of 0.1 decayed by factor of 0.1 the 100 and 250th epoch. A weight decay of 0.0001 is also applied.
ImageNet. We use a batch-size of 256 and train for 100 epochs. We apply center crop augmentation for testing per the common practice (i.e. resize image 256 x 256 and take the center crop of size 224 x 224) (He et al. 2016 NVIDIA 2022).
Waterbirds / CelebA We use the same model architecture and hyperparameters as Liu et al. (2021) in order enable a fair and direct comparison. All experiments are based on the default ResNet-50 architecture. The Waterbirds models are trained for 300 epochs using SGD with an initial learning rate of 0.00001, and high weight docay of 1.0. The model was carly-stopped after the 60th epoch for JTT (Liu et al. 2021). The CclebA models are trained for 50 epochs using SGD with an initial learning rate O. 0.00001, and high weight
decay of 0.1. The model was early-stopped after the first epoch for JTT (Liu et al. 202
Clothing We use the online batch selection protocol from Mindermanu et al. 2022 where 32 examples are chosen from a large batch of 320 examples for training at each step. Following Mindermann et al. (022),, use Adam W optimizer with default hyperparameters as in Py'oorch (Paszke et al. 2019 and ImageNet pretrained ResNet-50. No learning rate
decay applied in this case.
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Figure Demonstration of the effectiveness of MAP-D in detecting minority-group samples on two famous minorily-group datasets with spurious correlations, compared to the detection performance of JTT (Luu et al. (021) which relies on sarly-stopping. MAP-D achieves better or similar performance, without needing costly hyperparameter tuning or retraining.
B EXAMPLES CONSIDERED TO BE MISLABELED BY MAP-D
Fig. highlights images which MAP-D considers to be mislabeled from different ImageNet classes on the ImageNet train set. MAP-D is naturally very selective in considering examples to be mislabeled in contrast to conventional classifier-based approaches, which will assign an equal proportion of examples to the mislabeled set.
C BINARY VS. PROBABILISTIC OUTPUTS IN LABEL CORRECTION
Arazo et al. 2019) used convex combination of the labels weighted by the actual probability returned by their BMM model. As MAP-D returns probability estimates, this enabled leveraging label correction framework in the same way. However, the utility of the uncertainty estimates is not immediately apparent. Therefore, in order to gauge the utility of these uncertainty estimates, used binary predictions (argmax) instead of the actual probabilities returned by MAP-D. The results are visualized in Fig. g It clear from the figure that the model struggles significantly in coping with noise when being restricted to binary predictions, indicating that the uncertainty estimates provided by MAP-D enables the model to learn the correct label.
D NUMBER OF EPOCHS BEFORE LABEL CORRECTION
Arazo et al. 2019) segregated the complete 300 epochs of training into two phases: (i) pretraining phase where they train the model without label correction, and (ii) label correction phase for the rest of the 195 epochs during which they perform label correction. We observe that relative strength of MAP-D the ability to forgo such prolonged pretraining phase during training. We perform a simple experiment with a reduced number of pretraining epochs (10 instcad of 105), leading to 290 epochs of training with label correction. These results are presented in Fig. 10 demonstrating that there is only negligible impact of pretraining schedule on MAP-D performance, while the performance of Arazo et al. (019 is drastically impacted, specifically in no-noise and high-noise regimes.
E PROBE LOSS DISTRIBUTION
We plot the probe loss distribution at specific epochs during training in Fig. 11 The figure also presents loss distribution from the validation/tes teestt probe examples where which are not directly used for the nearest neighbor classifier. Initially, the loss distribution for different probe categories is similar. However, training progresses, the loss of the easier
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Typical
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Balloon
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Milk Can
Figure 8: Examples surfaced through the use of MAP-D on ImageNet train set using the Typical probe (first image in each set, highlighted with black border) and Random Output probe (next two images). Sub-caption indicates the ground truth class. This showcases the utility of MAP-D for exploring dataset, showing what the model considers typical for class as well as uncovering potentially problematic examples.
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Figure Ablation for label correction on CIFAR-10, where we use binary prediction instead of probability estimates returned by MAP-D. This highlights the utility and effectiveness of the uncertainty estimates computed by MAP-D.
probe categories converges to zero. This illustrates that distinguishing examples based loss distributions is possible, but difficult by just looking at one particular point in training. However, looking the complete loss trajectory is sufficient to disambiguate the defined probe categories. We visualize the loss trajectories for all the defined probe categories in Fig. 12
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Figure 10: Comparison between pretraining schedules 105 epochs (default value as set by Arazo et al. (019 and 10 epochs. Mean and standard deviation reported over 3 random runs. MAP-D is robust against changes the number of pretraining epochs, while the method from Arazo et al. (2019) achieves slightly poorer performance in the low-noise setting and significantly poorer perlormance in the high-noise setting.
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Figure 11: Probe categories are distinguishable via learning dynamics of a ResNet-50 trained ImageNet, validating the approach of MAP-D. For each of the probe categories, plot the spread losses at various epochs of training, including the validation/ten probe examples. However, it is worth noting that deciding single to distinguish these examples is difficult. On the other hand, using a full trajectory is sufficient for identification.
The figure also temptss to highlight that the loss distribution is similar for both training as well validation/tess probe examples from each of the probe categories.
F RELATED WORK (COMPLETE)
Many research directions focus on the properties of data and leveraging them in turn to improve the training process. We categorize and discuss each of these below.
Monitoring per-example learning dynamics Kaplun et al. (0022) tracked training each point individually in the dataset from multiple models in order to vield interesting insights into the structure of both models and data. Similarly, Rabanser et al. (2022) proposed confident classification framework via monitoring the disagroement between different chockpoints throughout the training process and rejected predictions on samples with significant disagreement. MAP-D is similar in spirit to this line of work, where we monitor loss val values instead of soflmax scores or model predictions on each individual example. However, MAP-D provides more general framework infer hidden metadata categories leveraging these training dynamics.
Difficulty of examples Koh and Liang (017 proposes influence functions to identify
training points most influentaa on given prediction. Work by Arpit et al. (0177); Li et al. (2020c): Feldman (2019): Feldman and Zhang (2020) develop methods that measure the
degree memorization required of individual examples. While WhileJiang et al. 2020 proposes
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Figure 12: Sanity check showing performance of MAP-D on the probe suite test set with all probe categories using ResNet-50 on ImageNet, where we know the ground-truth metadata. (a) Solid line shows the mean learning curve while translucent lines are randomly sampled 250 individual trajectories for each probe category. Again, the separation of different probes evident both in the dynamics over the course of training. (b) show confusion matrices between the true predicted metadata features, demonstrating strong performance of the probes.
consistency score rank each example by alignment with the training instances, Carlini et al. (2019) considers several different measures to isolate protolypes that could conceivably be extended rank the entire dataset. Agarwal al. (2021) leverage variance gradients across training to rank examples by learning difficulty. Further, Hooker et al. (2019) classify sensitivity to varying model capacity. contrast to all these approaches that attempt to rank an example along one axis, MAP-D is able to discern between different sources of uncertainty without any significant computational cost by directly leveraging the training dynamics of the model.
examples as challenging according
Coreset selection techniques The aim of these methods is to find prototypical examples
that represent larger corpus of data (Zhang 1992 Bien and Tibshirani 2012 Kim et al. 2015: Kim et al. 2016), which can be used to speed up training (Sener and Savaresc 2018 Shim et al. 2021 Huggins et al., 2017 Sorscher et al. 2022) or aid in interpretability of the MAP-D provides a computationally feasible alternate
model predictions Yoon et al. 2019).
to identify and surface these coresets.
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Published as a conference paper at ICLR 2023
Noisy examples A special case of example difficulty is noisy labels, and correcting for their presence. Arazo et al. (2019) use parameterized mixture models with two modes (for clean and noisy) fit to sample loss statistics, which they then use to relabel samples determined to be noisy. Li et al. (2020b) similarly uses mixture models to identify mislabelled samples, but actions on them by discarding the labels entirely and using these samples for auxiliary self-supervised training. These methods are unified by the goal of identifying examples that the model finds challenging, but unlike MAP-D, do not distinguish between the sources of this difficulty. The method from Arazo ct al. (2019) particularly works under the assumption of uniform label noise. Other works such as Northcutt et al. (2021) tackles the case of non-uniform noise by estimating the noise matrix.
Leveraging training signal There are several prior techniques that also leverage network
training dynamics over distinct phases of learning (Achille et al. 2017 Jiang et al. 2020
Mangalam and Prabhu, 2019: Faghri et al., 2020: Agarwal et al. 2021 p. Notably, Pleiss el al. 2020) use loss dynamics of samples over the course of training, but calculate an Area- Margin metric and show it can distinguish correct but difficult samples from mislabelled samples, In contrast, MAP-D is capable of inferring multiple data properties. Swayamdipta et al. (2020) computed the mean and variance of the model's confidence for the target label throughout training to identify interesting examples in the context of natural language processing. However, their method limited in terms of identifying only easy, hard, or confusing examples. Our work builds upon this direction and can be extended to arbitrary sources of uncertainty based on defined probe suites leveraging loss trajectories.
Adaptive training Adaptive training leverages training dynamics of the network to identify examples that are worth learning. Loss-based prioritization (Jiang et al. 2019 Katharopoulos and Fleuret 2018) upweight high loss examples, assuming these examples are challenging yet learnable. These methods have been shown to quickly degrade in performance in the presence of even small amounts of noise since upweighting noisy samples hurts generalization (Hu et al. 2021 Paul et al. 2021). D'souza et al. (2221) motivate using targeted data augmentation to distinguish between different sources of uncertainty, and adapting training based upon differences in rates of learning. On the other hand, several methods prioritize learning on examples with a low loss assuming that they are more meaningful to learn. Recent work has also attempted to discern between points that are lcarnable (not noisy), worth learning (in distribution), and not yet learned (not redundant) (Mindermann al. 2022). MAP-D can also be leveraged for adaptive training by defining the different sources of uncertainties of interest.
Minority group samples The recent interest has been particularly towards finding and dealing with minority group samples to promote model fairness (Sagawa et al. 2019 2020
Liu et al.) 2021 Zhang et al.) 2022 Nam et al., 2022). The dominant approach to deal with this problem without assuming access to group labels is either pseudo-label the dataset using classifier (Nam et al. 2022) or to train a model with carly-stopping via small validation set to surface minority group samples (Liu al. 2021 Zhang et al., 2022). However, this setting only works for the contrived datasets where the model can classity the group based on the background. MAP-D leverages the population statistics rather than exploiting the curation process of the dataset to naturally surface minority group samples, which is scalable and applicable in the real-world.
G LIMITATIONS AND FUTURE WORK
MAP-D surfaces examples from the model based on the loss trajectories. This is based on strong assumption that these loss trajectories are separable. It is possible that the learning curve for two set of probe categories exhibit similar behavior, limiting the model's capacity in telling them apart. In this case, the learning curve is no longer a valid discriminator between probes.
Furthermore, developing an appropriate probe-suite for a given task is non-trivial. As we use automated techniques, the effectiveness of the curated probe suite be low for certain
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Published as a conference paper at ICLR 2023
applications. Furthermore, the automated techniques leveraging for designing the probe suite might not be applicable for a particular modality. In practice, designing a good and appropriate probe suite which elicits the right information from the model is a difficult task.
However, for good constructions of probe categories relying on global population statistics, consider MAP-D be competitive and data-cfficient method.
This work is focused on a computer vision sctting; we consider an important direction of future work to be extending this other domains such as speech or NLP.
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Reviews:
[{"review_id": "RgV6QNT3VEh", "reviewer": "Reviewer_o59Z", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper provides a method to infer various meta-data features of datasets such as typical/atypical data points, noisy labels, noisy and out-of-distribution data points. ", "summary_of_the_review": "I like the paper, I think it is novel, simple, intuitive, well-written with strong experimental results, but can benefit from some more experiments and clarifications. ", "strength_and_weaknesses": "*Strengths*:\n1. The paper proposes, to the best of my knowledge, the first method which jointly infers multiple meta-data features of a dataset. \n2. Simplicity: The method is simple and intuitive. \n3. Paper is very well-written. \n\n*Weaknesses*:\n1. While I do appreciate the simplicity of the methods, it is hard to believe that the method performs on par with state-of-the-art label correction methods. I believe this can only be the case when the label corruptions occur uniformly at random, which might very well not be the case, as shown by many recent paper on noisy label correction. Moreover, the papers that the authors compare with are not the state-of-the-art since methods like Confident learning proposed later outperform the methods the authors compare with. \n2. The method does not seem to detect out-of-distribution samples very well (Fig. 4 (d)). I would like the authors to explain the underwhelming performance. \n3. It would be nice and instructive to discuss failure of the method in detecting these meta-data features. Like the authors mention in the limitations, the assumption that the learning dynamics.\n4. The authors mention experiments on two different kinds on ResNet models, but (1) I do not see results from both the models, (2) experimenting with different model architectures might make for a stronger experimental result, showing the method's applicability beyond ResNet architectures. \n\n*Questions*\n1. How are out-of-distribution probes generated? ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "N4m0p6Z6E9", "reviewer": "Reviewer_Kiey", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "**Note: Score updated from 6 to 8 after authors' response.**\n\nThe authors propose a data sample annotation method they term Metatdata Archaeology via Probe Dynamics (MAP-D). In this method, one first creates data subsets with known properties (“probes”), such as corrupted, mislabeled, and atypical/typical samples. One then checks whether other samples have these properties by performing kNN classification on the time series of losses of unmodified data samples, with the time series of losses of the probes serving as the cluster labels. The authors apply MAP-D to six different image classification datasets and find that MAP-D performs similarly to SOTA baselines for identifying and correcting mislabeled examples, and can be used to speed up training by prioritizing training samples.", "summary_of_the_review": "Straightforward method that shows promise for multiple different applications. I have a few suggestions to improve clarity and one to more convincingly measure a claimed training speed-up, but overall great work.", "strength_and_weaknesses": "**Strengths**\n\nSimple method\n\nConvincing results; method seems promising for multiple different applications\n\nWell-written and mostly easy to follow\n\n**Weaknesses**\n\nTraining speed-ups not measured in wall clock time\n\nA few clarity issues, mostly regarding explanation of the noise correction methodology\n\nSome of the more important content is in the Appendix, and some of what I would consider follow-up experiments are presented in the main text", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "Hkj8DSbudz", "reviewer": "Reviewer_3RHf", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper introduces MAP-D, a novel approach to data archeology that leverages training dynamics to uncover a dataset's salient meta-data. In contrast to already existing approaches, MAP-D enables the simultaneous auditing of a dataset across multiple dimensions (e.g., typical data, corrupted inputs, etc).", "summary_of_the_review": "The paper introduces a novel, simple, effective approach to an important problem: meta-data archeology. Overall, it is well written and organized, and it makes a strong empirical case. The paper could be further improved by (i) adding more in-depth discussions of the results, and (ii) further emphasize MAP-DF's impact on noise correction, prioritizing points for training, and identifying minority-group samples.", "strength_and_weaknesses": "The paper's main strengths are the novelty, simplicity, and effectiveness of MAP-D. It is also reasonably well-written and organized.\n\nThe paper's main weakness is the lack of an organic, real-world dataset on which to compare MAP-D against a high-quality, human-annotated test set. The probes described in section 2.2 (i.e., typical, atypical, random labels, random input & labels, and corrupted inputs) are intuitive and easy to compute w/o human annotations. However, understanding MAP-D's performance against a human-curated test set would make the paper significantly more compelling, while also highlighting its practical strengths and weaknesses in a real-world scenario.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "EZVgqYy2M-9", "reviewer": "Reviewer_KmwB", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes Metadata Archaeology, a unifying and general framework for uncovering latent metadata categories.\nThis paper introduces and validate the approach of Metadata Archaeology via Probe Dynamics (MAP-D): leveraging the training dynamics of curated data subsets called probe suites to infer other examples’ metadata.\nThis paper shows how MAP-D could be leveraged to audit large-scale datasets or debug model training, with negligible added cost. This is in contrast to prior work which presents a siloed treatment of different data properties.\nThe authors use MAP-D to identify and correct mislabeled examples in a dataset. Despite its simplicity, MAP-D is on-par with far more sophisticated methods, while enabling natural extension to an arbitrary number of modes.\nThe authors show how to use MAP-D to identify minority group samples, or surface examples for data-efficient prioritized training.", "summary_of_the_review": "See Strength And Weaknesses", "strength_and_weaknesses": "Pros: \n1. The motivation is clear. \n2. The paper is well-written and organized. \nCons: \n1. The main contributions are not clear. \n2. Some related works are missing, e.g., 3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: notable-top-25%