Facial age estimation by deep residual decision making

Kwang-Ting Cheng; Shichao Li

REVIEW 3 major objections 6 minor 38 references

Facial age estimation by deep residual decision making

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read By adding residual shortcuts to neural decision forests, the model matches state-of-the-art age estimation accuracy while consuming 4.8x less memory and 4x less compute.

desk verdict Solid engineering extension of deep regression forests with real efficiency gains, but the paper's central residual-learning claim is untested because the comparison changes both the shortcuts and the backbone. read the letter →

arxiv 1908.10737 v1 pith:CUAWXXCK submitted 2019-08-28 cs.CV cs.LGeess.IV

Shichao Li , Kwang-Ting Cheng This is my paper · ORCID

classification cs.CVcs.LGeess.IV

keywords facialageestimationdeepneuraldecisionforestresiduallearningsaliencymapregressionmodelefficiencysofttrees

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper brings residual learning—the skip-connection trick that helps train very deep neural networks—into deep neural decision forests (NDFs), where trees made of differentiable splitting functions learn to route an image to a leaf prediction. On three facial age estimation benchmarks, the resulting model, called RNDF, lands at state-of-the-art-level accuracy: it trails the prior deep regression forest (DRFs) slightly on FG-NET and MORPH, beats it on CACD, and does so with about 4.8x smaller model size and 4x fewer FLOPs. The paper also introduces decision saliency maps, gradients of routing probabilities with respect to the input, to expose which facial regions drive each decision. The central claim is that residual learning helps optimize the soft decision functions themselves, not just the feature extractor, and that this makes accurate age estimation cheaper to run.

What carries the argument

The central mechanism is the residual mapping inserted into each feature extraction stage of the neural decision forest: $M_k(x)=x+H_k(x)$, where $H_k$ is the learned transformation inside one residual bottleneck block. A deep neural decision forest is an ensemble of binary decision trees whose splitting nodes compute differentiable routing probabilities; the final prediction is the convex combination of leaf predictions weighted by path probabilities, and the paper sets each routing score as $s_i=\sigma(f_i(M_n(\dots M_1(x))))$. The identity shortcut is the load-bearing component claimed to ease optimization, allowing a ResNet50-like feature extractor to feed five depth-6 trees, with Gaussian covariance updates for leaf nodes alternating with gradient descent on the network parameters.

What would settle it

Train the same residual-block network with the shortcut connection removed, so $M_k(x)=H_k(x)$ instead of $M_k(x)=x+H_k(x)$, keeping all other architecture, training, and data settings identical; if MAE, model size, and FLOPs stay the same, the residual mapping is not the cause of the reported gains.

Watch

Extended reading notes

Core claim

The paper's central claim is that replacing plain feature mappings in a deep neural decision forest with residual mappings $M_k(x)=x+H_k(x)$ makes the soft routing functions easier to optimize. The resulting residual neural decision forest (RNDF) reports mean absolute errors of 2.97 on FG-NET (against 2.91 for DRFs), 3.87 on MORPH (against 3.85), and 4.595 on CACD (against 4.637), while model size drops from 539.4 MB to 112.4 MB and FLOPs drop from 16 G to 4 G. The paper further derives decision saliency maps, $\partial s_i/\partial x$, to visualize which image regions influence each routing decision along the most probable computation path.

Load-bearing premise

The paper credits residual learning for its gains, but the comparison model also uses a different feature-extraction network, so the residual shortcut itself is never tested on its own.

Editorial extensions

If this is right

Facial age estimation can run with 4.8x less memory and 4x fewer FLOPs than the previous best neural-forest model at nearly unchanged accuracy, easing deployment on embedded or mobile devices.
Decision saliency maps make the forest's routing choices inspectable, so a user can check whether the model keys on facial skin rather than hair or background.
Residual learning transfers from plain CNNs to neural decision forests, suggesting the same shortcut can benefit other NDF regression tasks.
Because routing in this design uses a single shared fully-connected layer, the model can be extended by assigning splitting nodes to different feature layers or by learning non-binary tree topologies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Our inference: the reported savings may come largely from switching the feature-extraction network to a ResNet-style backbone, since the non-residual baseline uses a VGG-style network; an ablation with only the shortcuts removed would isolate the residual effect.
Our inference: aggregating decision saliency maps across all trees and leaves, rather than only the most probable path, could produce a more complete explanation heatmap and is a natural extension.
Our inference: if residual shortcuts are the cause, the same shortcut should improve other neural decision forest baselines, which is a testable prediction beyond the present benchmarks.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Quick take: this is a competent, honest engineering paper that swaps the feature extractor in an existing deep regression forest (DRFs) for a ResNet50-style block and reports meaningful efficiency gains at roughly equal accuracy. The main flaw is that the central mechanism — residual learning — is never isolated, so the title's promise is not actually supported. The stress-test note gets this exactly right.

What's new and what it does well. As far as I can tell, this is the first time residual connections have been used inside an NDF for age estimation, and the first time gradient-based saliency maps have been computed on routing probabilities. Those are modest firsts, but they are real. The experiments cover three standard benchmarks (FG-NET, MORPH, CACD), the implementation looks clean, and the efficiency numbers (4.8x smaller model, 4x fewer FLOPs) are credible. Accuracy is roughly on par with DRFs: slightly worse on FG-NET, slightly better on MORPH cumulative score, and better on CACD by 0.04 MAE. That is a reasonable trade-off for a 4-5x cheaper model.

Where the soft spots are. The big one, as noted, is the missing ablation. RNDF differs from DRFs in two ways at once: residual shortcuts and a ResNet50-like backbone instead of VGG-16. The accuracy differences and the efficiency gains could entirely come from the backbone change. Running a plain ResNet50-style NDF without the shortcuts would settle it. The introduction overclaims by saying "state-of-the-art accuracy" when Table 1 shows the model trails DRFs on FG-NET MAE and CS and on MORPH MAE; the abstract's "state-of-the-art level" is the fairer phrasing. There are also no error bars or significance tests, so the small metric differences are likely within noise. The saliency analysis is a standard gradient technique applied to routing scores; the observations are plausible but not deep.

Bottom line: this is a useful data point for people working on efficient age estimators or on NDFs in general. It is not a breakthrough, but it is not a waste of time either. The missing ablation is addressable, not load-bearing in the sense of invalidating the efficiency claim — it just means the residual mechanism remains unproven.

My recommendation: send it to peer review, but with the clear expectation that the authors either run the shortcut-removal ablation or reframe the contribution as "an efficient NDF-based estimator" rather than a validation of residual learning for NDFs. A serious referee would catch the gap, so the paper deserves the chance to fix it rather than a desk reject.

Best, [You]

Referee Report

3 major / 6 minor

Summary. The paper proposes Residual Neural Decision Forest (RNDF), which incorporates residual connections into the feature extractor of a deep neural decision forest, and applies it to facial age estimation on FG-NET, MORPH, and CACD. The authors report accuracy comparable to or slightly better than the prior DRFs model on CACD, with 4.8x memory savings and 4x FLOPs savings, and also introduce decision saliency maps (DSMs) to visualize the routing decisions. The abstract and introduction claim state-of-the-art accuracy and a first application of residual learning to NDF, with code and pre-trained models to be released.

Significance. If the central claim is validated, the contribution is an efficient age estimator with accuracy competitive with a strong prior model, plus a novel visualization method for NDF inference. The paper is clearly written, the training procedure is fully specified, and the promised code release is a concrete asset. However, the key attribution of the gains to residual learning is not supported by the experiments as presented: the comparison to DRFs changes both the residual connections and the backbone architecture at once, so the title's mechanism is not isolated. The accuracy differences from DRFs on two of three benchmarks are small and go in opposite directions, and no error bars are reported. The visualization results are qualitative and would benefit from a more rigorous assessment. With an added ablation and toned-down claims, the paper could be a solid empirical contribution.

major comments (3)

[Section 4.3, Tables 1 and 2] The comparison to DRFs cannot support the claim that residual learning improves NDF, because RNDF and DRFs differ in two variables at once: RNDF uses a ResNet50-like feature extractor with shortcut connections, while DRFs uses VGG-16. No ablation removes only the shortcut connections from RNDF while keeping the remaining architecture identical. The reported accuracy differences (RNDF trails DRFs by 0.06 MAE on FG-NET and by 0.02 MAE and 4.5 CS points on MORPH, and leads by 0.042 MAE on CACD) and the 4.8x memory and 4x FLOPs savings in Table 2 could be caused entirely by the backbone change. The central contribution of the paper, incorporating residual learning into NDF, is exactly the factor left uncontrolled; please add an ablation with the same architecture but with the shortcut connections removed (or an equivalent control) before the title's mechanism can be credited.
[Abstract and Section 1] The abstract states the model 'achieves state-of-the-art level accuracy' and the introduction claims it 'achieves state-of-the-art accuracy', but Table 1 shows RNDF does not beat DRFs on FG-NET (2.97 vs. 2.91 MAE) or on MORPH (3.87 vs. 3.85 MAE; 76.1% vs. 80.6% CS). Only on CACD does RNDF improve over DRFs (4.595 vs. 4.637 MAE), and even there the difference is small. The claim should be restricted to 'comparable accuracy on some benchmarks with a large efficiency gain', or the experiments should be extended to support a true state-of-the-art claim.
[Section 4.3 and Table 1] No error bars or statistical significance tests are reported. The MORPH results are averages over 5 random splits, but the variance across splits is not given, and the differences with DRFs are small (0.06, 0.02, and 0.042 MAE). Without variance information or a paired test, it is impossible to tell whether RNDF is actually better, worse, or equivalent to DRFs on any of the benchmarks. Please report standard deviations (and, if available, the variance of the DRFs results under the same protocol) or otherwise justify that the differences are meaningful.

minor comments (6)

[Figure 1 caption] Typo: 'Redisual learning' should be 'Residual learning'.
[Section 3.1, Eq. (4)] The definition Mk(x) = x + Hk(x) is informal: it should be stated that x is the input to the block and Hk is the residual mapping (e.g., a stack of convolution layers), and that the addition is performed element-wise on the feature maps; otherwise the notation is ambiguous.
[Section 3.2, Eq. (11)] The decision saliency map is defined as the gradient of the routing probability si with respect to the input, but it is not specified which si is used when multiple splitting nodes are on the traced path; clarify whether the gradient is computed for each node individually and how the maps in Figures 3 and 4 are combined.
[Section 4.2, Model Architecture] The description 'Resnet50-like architecture' is too vague for reproducibility; please provide the exact block layout (number of layers per stage, filter sizes, strides) or refer the reader to a specific configuration in the released code.
[Section 4.3, discussion of Figures 3 and 4] The statement that the model 'is not sensitive to pre-processing' based on the qualitative DSMs (larger face regions in CACD, smaller in MORPH) is not supported by any quantitative comparison; please either add a controlled experiment or tone down the claim.
[Throughout] Minor typos and inconsistent wording appear, e.g., 'paramters' in Algorithm 1 line 2, 'which is a another type' in Section 2, and inconsistent use of 'state-of-the-art level accuracy' vs. 'state-of-the-art accuracy'; please proofread carefully.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: RNDF is evaluated against external benchmarks with standard training; the residual-learning attribution is a confound, not a circular reduction.

full rationale

The paper's derivation chain is self-contained. The model is defined by Eq. 3-4 (routing scores through feature extractors with residual mapping Mk(x) = x + Hk(x)) and trained with the squared loss (Eq. 5), gradient backpropagation (Eq. 6), and leaf-node updates (Eq. 7-10) adopted from the external DRFs work [29]. No fitted parameter is presented as a prediction: accuracy on FG-NET, MORPH, and CACD is measured on held-out subjects/images, and the comparison against DRFs [29] is an external baseline from a different group. The only self-citation, [18], is a pointer to the authors' prior workshop paper about visualizing decision forests for image classification; it is not used to justify any load-bearing claim in this paper. The absence of an ablation isolating shortcut connections from the ResNet50-like backbone is a legitimate experimental-design concern about attributing gains to residual learning, but that is a confound (an uncontrolled variable), not a circularity: the claimed prediction is not equivalent to the input by construction, nor is any result forced by a self-citation chain. Under the hard rules, this concern belongs to correctness risk rather than circularity analysis, so the circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper does not derive any constants or physical quantities; the listed free parameters are training hyperparameters, none fitted to data. The axioms are the standard NDF assumptions and the paper's own residual-learning hypothesis, which is not proved here.

free parameters (4)

number_of_trees = 5
Chosen to match DRFs [29] for fair comparison; not fitted.
tree_depth = 6
Chosen to match DRFs [29]; not fitted.
initial_learning_rate = 0.5
Chosen by hand for SGD; standard schedule halving on plateau.
leaf_update_batch_interval = 50
Leaf node parameters updated after every 50 batches; hand-picked.

assumptions (4)

domain assumption Residual learning eases optimization of deep networks, based on loss landscape visualization [17].
The paper's central hypothesis is adopted from prior work, not proved within this paper.
standard math Soft decision tree routing is differentiable and trainable by gradient descent.
Standard NDF formulation used in Equations (1)-(3).
domain assumption The leaf node update rule from [29] has guaranteed loss reduction.
Adopted without re-derivation; the guarantee is cited to prior work.
domain assumption Leaf node predictions follow a Gaussian distribution with learned mean and covariance.
Assumed in Equations (7)-(10) as a modeling choice for regression.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Facial age estimation by deep residual decision making." pith.science (2026). https://pith.science/paper/CUAWXXCK

@misc{pith2026190810737,
  author       = {Pith},
  title        = {Pith review of: Facial age estimation by deep residual decision making},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CUAWXXCK}},
  note         = {Machine review of arXiv:1908.10737}
}

read the original abstract

Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks. However, it has not been applied to deep neural decision forest (NDF). In this paper we incorporate residual learning into NDF and the resulting model achieves state-of-the-art level accuracy on three public age estimation benchmarks while requiring less memory and computation. We further employ gradient-based technique to visualize the decision-making process of NDF and understand how it is influenced by facial image inputs. The code and pre-trained models will be available at https://github.com/Nicholasli1995/VisualizingNDF.

Figures

Figures reproduced from arXiv: 1908.10737 by the authors.

**Figure 2.** Detailed architecture of RNDF. The input goes through four types of residual [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Decision saliency maps for CACD. Each row gives the decision-making process [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Decision saliency maps for Morph using the same annotation style as Figure [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 18 canonical work pages

[1]

Anchored regression networks applied to age estimation and super resolution

Eirikur Agustsson, Radu Timofte, and Luc Van Gool. Anchored regression networks applied to age estimation and super resolution. In The IEEE International Conference on Computer Vision (ICCV), Oct 2017

work page 2017
[2]

Bulat and Y

A. Bulat and Y . Tzimiropoulos. Hierarchical binary cnns for landmark localization with limited resources. IEEE Transactions on Pattern Analysis and Machine Intelligence , pages 1–1, 2018. ISSN 0162-8828. doi: 10.1109/TPAMI.2018.2866051. 10 LI, CHENG: FACIAL AGE ESTIMA TION BY DEEP RESIDUAL DECISION MAKING

arXiv 2018
[3]

Chang, C

K. Chang, C. Chen, and Y . Hung. A ranking approach for human ages estimation based on face images. In 2010 20th International Conference on Pattern Recognition , pages 3396–3399, Aug 2010. doi: 10.1109/ICPR.2010.829

work page doi:10.1109/icpr.2010.829 2010
[4]

Chang, C

K. Chang, C. Chen, and Y . Hung. Ordinal hyperplanes ranker with cost sensitivities for age estimation. In CVPR 2011, pages 585–592, June 2011. doi: 10.1109/CVPR.2011. 5995437

doi:10.1109/cvpr.2011 2011
[5]

B. Chen, C. Chen, and W. H. Hsu. Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Transactions on Multimedia , 17(6):804–815, June 2015. ISSN 1520-9210. doi: 10.1109/TMM.2015.2420374

arXiv 2015
[6]

K. Chen, S. Gong, T. Xiang, and C. C. Loy. Cumulative attribute space for age and crowd density estimation. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 2467–2474, June 2013. doi: 10.1109/CVPR.2013.319

work page doi:10.1109/cvpr.2013.319 2013
[7]

X. Geng, Z. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence , 29 (12):2234–2240, Dec 2007. ISSN 0162-8828. doi: 10.1109/TPAMI.2007.70733

arXiv 2007
[8]

X. Geng, C. Yin, and Z. Zhou. Facial age estimation by learning from label distri- butions. IEEE Transactions on Pattern Analysis and Machine Intelligence , 35(10): 2401–2412, Oct 2013. ISSN 0162-8828. doi: 10.1109/TPAMI.2013.51

doi:10.1109/tpami.2013.51 2013

Show all 38 references

[9]

G. Guo, Y . Fu, C. R. Dyer, and T. S. Huang. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing, 17(7):1178–1188, July 2008. ISSN 1057-7149. doi: 10.1109/TIP.2008. 924280

2008 doi
[10]

H. Han, C. Otto, X. Liu, and A. K. Jain. Demographic estimation from face images: Human vs. machine performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(6):1148–1161, June 2015. ISSN 0162-8828. doi: 10.1109/TPAMI. 2014.2362759

2015
[11]

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016

2016
[12]

Densely connected convolutional networks

Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017

2017
[13]

Kazemi and J

V . Kazemi and J. Sullivan. One millisecond face alignment with an ensemble of re- gression trees. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 1867–1874, June 2014. doi: 10.1109/CVPR.2014.241

2014 doi
[14]

Multimodal residual learning for visual qa

Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung- Woo Ha, and Byoung-Tak Zhang. Multimodal residual learning for visual qa. In Advances in neural information processing systems , pages 361–369, 2016

2016
[15]

Interpretable learning for self-driving cars by visualizing causal attention

Jinkyu Kim and John Canny. Interpretable learning for self-driving cars by visualizing causal attention. In The IEEE International Conference on Computer Vision (ICCV) , Oct 2017. LI, CHENG: FACIAL AGE ESTIMA TION BY DEEP RESIDUAL DECISION MAKING 11

2017
[16]

Kontschieder, M

P. Kontschieder, M. Fiterau, A. Criminisi, and S. R. BulÃš. Deep neural decision forests. In 2015 IEEE International Conference on Computer Vision (ICCV) , pages 1467–1475, Dec 2015. doi: 10.1109/ICCV .2015.172

2015 doi
[17]

Vi- sualizing the loss landscape of neural nets

Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Vi- sualizing the loss landscape of neural nets. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Ad- vances in Neural Information Processing Systems 31 , pages 6...

2018
[18]

Visualizing the decision-making process in deep neural decision forest

Shichao Li and Kwang-Ting Cheng. Visualizing the decision-making process in deep neural decision forest. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 114–117, 2019

2019
[19]

S. Liao, A. K. Jain, and S. Z. Li. A fast and accurate unconstrained face detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2):211–223, Feb 2016. ISSN 0162-8828. doi: 10.1109/TPAMI.2015.2448075

2016
[20]

K. Luu, K. Seshadri, M. Savvides, T. D. Bui, and C. Y . Suen. Contourlet appearance model for facial age estimation. In 2011 International Joint Conference on Biometrics (IJCB), pages 1–8, Oct 2011. doi: 10.1109/IJCB.2011.6117601

2011
[21]

Understanding deep image representations by inverting them

Aravindh Mahendran and Andrea Vedaldi. Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5188–5196, 2015

2015
[22]

Montillo and H

A. Montillo and H. Ling. Age regression from faces using random forests. In 2009 16th IEEE International Conference on Image Processing (ICIP) , pages 2465–2468, Nov 2009. doi: 10.1109/ICIP.2009.5414103

2009
[23]

Overview of research on facial ageing using the fg-net ageing database

Gabriel Panis. Overview of research on facial ageing using the fg-net ageing database. IET Biometrics , 5:37–46(9), June 2016. ISSN 2047-4938. URL https://digital-library.theiet.org/content/journals/10. 1049/iet-bmt.2014.0053

2016
[24]

Age progression in human faces: A survey

Narayanan Ramanathan, Rama Chellappa, Soma Biswas, et al. Age progression in human faces: A survey. Journal of Visual Languages and Computing , 15:3349–3361, 2009

2009
[25]

Ricanek and T

K. Ricanek and T. Tesafaye. Morph: a longitudinal image database of normal adult age- progression. In 7th International Conference on Automatic Face and Gesture Recogni- tion (FGR06), pages 341–345, April 2006. doi: 10.1109/FGR.2006.78

2006 doi
[26]

Some like it hot - visual guidance for preference prediction

Rasmus Rothe, Radu Timofte, and Luc Van Gool. Some like it hot - visual guidance for preference prediction. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

2016
[27]

Deep expectation of real and apparent age from a single image without facial landmarks

Rasmus Rothe, Radu Timofte, and Luc Van Gool. Deep expectation of real and apparent age from a single image without facial landmarks. Interna- tional Journal of Computer Vision , 126(2):144–157, Apr 2018. ISSN 1573-

2018
[28]

Roy and S

A. Roy and S. Todorovic. Monocular depth estimation using neural regression forest. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5506–5514, June 2016. doi: 10.1109/CVPR.2016.594

2016 doi
[29]

W. Shen, Y . Guo, Y . Wang, K. Zhao, B. Wang, and A. Yuille. Deep regression forests for age estimation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2304–2313, June 2018. doi: 10.1109/CVPR.2018.00245

2018
[30]

Label distribution learning forests

Wei Shen, Kai Zhao, Yilu Guo, and Alan Yuille. Label distribution learning forests. In Advances in neural information processing systems , 2017

2017
[31]

Very deep convolutional networks for large- scale image recognition

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large- scale image recognition. arXiv preprint arXiv:1409.1556, 2014

2014 arXiv
[32]

Deep inside convolu- tional networks: Visualising image classiﬁcation models and saliency maps

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolu- tional networks: Visualising image classiﬁcation models and saliency maps. CoRR, abs/1312.6034, 2013. URL http://arxiv.org/abs/1312.6034

2013 arXiv
[33]

X. Wang, R. Guo, and C. Kambhamettu. Deeply-learned feature for age estimation. In 2015 IEEE Winter Conference on Applications of Computer Vision , pages 534–541, Jan 2015. doi: 10.1109/W ACV .2015.77

2015 doi
[34]

Zhang, W

K. Zhang, W. Zuo, Y . Chen, D. Meng, and L. Zhang. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing, 26(7):3142–3155, July 2017. ISSN 1057-7149. doi: 10.1109/TIP.2017. 2662206

2017 doi
[35]

Interpretable convolutional neural networks

Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu. Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8827–8836, 2018

2018
[36]

Interpreting cnns via decision trees

Quanshi Zhang, Yu Yang, Ying Nian Wu, and Song-Chun Zhu. Interpreting cnns via decision trees. arXiv preprint arXiv:1802.00121, 2018

2018 arXiv
[37]

Zhang and D

Y . Zhang and D. Yeung. Multi-task warped gaussian process for personalized age esti- mation. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2622–2629, June 2010. doi: 10.1109/CVPR.2010.5539975

2010
[1405]

URL https://doi.org/10.1007/ s11263-016-0940-3

doi: 10.1007/s11263-016-0940-3. URL https://doi.org/10.1007/ s11263-016-0940-3 . 12 LI, CHENG: FACIAL AGE ESTIMA TION BY DEEP RESIDUAL DECISION MAKING

doi

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Anchored regression networks applied to age estimation and super resolution

Eirikur Agustsson, Radu Timofte, and Luc Van Gool. Anchored regression networks applied to age estimation and super resolution. In The IEEE International Conference on Computer Vision (ICCV), Oct 2017

work page 2017

[2] [2]

Bulat and Y

A. Bulat and Y . Tzimiropoulos. Hierarchical binary cnns for landmark localization with limited resources. IEEE Transactions on Pattern Analysis and Machine Intelligence , pages 1–1, 2018. ISSN 0162-8828. doi: 10.1109/TPAMI.2018.2866051. 10 LI, CHENG: FACIAL AGE ESTIMA TION BY DEEP RESIDUAL DECISION MAKING

arXiv 2018

[3] [3]

Chang, C

K. Chang, C. Chen, and Y . Hung. A ranking approach for human ages estimation based on face images. In 2010 20th International Conference on Pattern Recognition , pages 3396–3399, Aug 2010. doi: 10.1109/ICPR.2010.829

work page doi:10.1109/icpr.2010.829 2010

[4] [4]

Chang, C

K. Chang, C. Chen, and Y . Hung. Ordinal hyperplanes ranker with cost sensitivities for age estimation. In CVPR 2011, pages 585–592, June 2011. doi: 10.1109/CVPR.2011. 5995437

doi:10.1109/cvpr.2011 2011

[5] [5]

B. Chen, C. Chen, and W. H. Hsu. Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Transactions on Multimedia , 17(6):804–815, June 2015. ISSN 1520-9210. doi: 10.1109/TMM.2015.2420374

arXiv 2015

[6] [6]

K. Chen, S. Gong, T. Xiang, and C. C. Loy. Cumulative attribute space for age and crowd density estimation. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 2467–2474, June 2013. doi: 10.1109/CVPR.2013.319

work page doi:10.1109/cvpr.2013.319 2013

[7] [7]

X. Geng, Z. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence , 29 (12):2234–2240, Dec 2007. ISSN 0162-8828. doi: 10.1109/TPAMI.2007.70733

arXiv 2007

[8] [8]

X. Geng, C. Yin, and Z. Zhou. Facial age estimation by learning from label distri- butions. IEEE Transactions on Pattern Analysis and Machine Intelligence , 35(10): 2401–2412, Oct 2013. ISSN 0162-8828. doi: 10.1109/TPAMI.2013.51

doi:10.1109/tpami.2013.51 2013