Pith. sign in

REVIEW 4 major objections 5 minor 32 references

Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read A DINO-pretrained, multi-branch Vision Transformer can act as a high-fidelity digital-twin surrogate for Drosophila midgut 3D+T confocal imaging, reconstructing tissue slices across depth and timepoints with low error and high structural si

desk verdict A useful reconstruction model with released code, but the 'digital twin for time-resolved dynamics' framing is not supported by the cross-sectional data. read the letter →

arxiv 2508.15883 v2 pith:GRXVEXX2 submitted 2025-08-21 eess.IV cs.AIcs.LGq-bio.TO

Kaan Berke Ugurlar , Joaquín de Navascués , Michael Taynnan Barros This is my paper · ORCID
classification eess.IVcs.AIcs.LGq-bio.TO
keywords 3D+TimagingconfocalmicroscopydigitaltwinsurrogatemodelingvisiontransformerDINOpretrainingepithelialhomeostasisDrosophilamidgut
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

The paper tries to establish that a vision-transformer-based network can serve as a high-fidelity surrogate for 3D+T confocal microscopy of the Drosophila midgut: given multi-view z-stack frames, the VT-DTSN reconstructs the corresponding tissue image with low pixel error, high structural similarity, and preserved feature-level alignment across all 18 optical layers and eight biological replicates. The point of such a surrogate is to let researchers run in silico experiments, test perturbation effects, generate hypotheses, and prioritize wet-lab conditions without needing a mechanistic simulator or continuous live imaging. The authors are explicit that the dataset's timepoints are cross-sectional snapshots from separate flies at days 4, 8, and 12, so 'temporal' here means variability across biological replicates rather than a time-lapse of one midgut. If the reconstruction claim holds, the approach offers a fast, near-real-time computational proxy for tissue imaging that could complement or partially replace repeated physical measurements.

What carries the argument

The load-bearing object is the VT-DTSN architecture itself. Three DINO-pretrained ViT-B/8 encoders (patch size 8, embedding 768, 12 layers, 12 heads) process overlapping lateral crops from each z-slice; their token-level features are mean-pooled from the last block, concatenated to a 2304-dimensional vector, and fused by an MLP into a unified representation that a reconstruction head turns into the predicted slice. DINO pretraining is meant to give the encoders features robust to intensity shifts and staining variability, while the multi-view construction lets the model combine lateral context with adjacent-z information. The composite loss L = αMSE + βSSIM + γCosine ties training directly t

What would settle it

Image the same midgut repeatedly over days 4, 8, and 12 (within phototoxicity limits) and compare the model's predicted t+Δt stack with the observed stack; if prediction error is no better than simply copying the current frame, or if cross-day predictions are indistinguishable from shuffled replicate labels, the temporal-dynamics claim is falsified.

Watch

Extended reading notes

Core claim

On its own terms, the central discovery is that a three-branch Vision Transformer—each branch a DINO-pretrained ViT-B/8 consuming an overlapping left/mid/right crop of a z-slice, with features concatenated and fused through an MLP and a reconstruction head—learns the mapping from input stacks to high-fidelity target images. Trained with a composite loss of MSE, SSIM, and cosine similarity, the model reports average MSE 9.33, SSIM 0.871, and cosine similarity 0.848 across layers; SSIM stays in the 0.84–0.92 range, and the model preserves cellular boundaries, nuclear regions, and marker-defined classes on held-out replicates. The authors position VT-DTSN not as a mechanistic digital twin with

Load-bearing premise

The claim that the model reconstructs 'time-resolved dynamics' rests on treating images from separate flies at days 4, 8, and 12 as a temporal trajectory, even though each timepoint is an independent specimen with no repeated imaging of the same midgut.

Editorial extensions

If this is right

  • A trained VT-DTSN can produce depth- and time-consistent reconstructions from new, possibly sparse or noisy z-stacks at interactive speeds after pruning and quantization, enabling feedback during live imaging sessions.
  • Because the DINO-pretrained features and multi-view fusion are designed to tolerate contrast and intensity variability, the surrogate should generalize across biological replicates without per-sample retraining.
  • The reconstructed volumes retain feature-space alignment, so they can feed downstream segmentation, cell-type classification, and tracking pipelines without losing biological information.
  • Researchers could use the surrogate to simulate genetic, pharmacological, or mechanical perturbations in silico and rank which experimental conditions are worth testing in vivo.
  • Since the framework does not require mechanistic parameterization, the same architecture is claimed to be adaptable to other tissues and imaging modalities.

Reading between the lines

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

  • The 'temporal' component is currently cross-sectional across specimens; before using this as a true digital twin for future-state prediction, the authors would need to validate it on longitudinal imaging of the same midgut, where the phototoxicity constraints the paper cites are exactly the obstacle.
  • An ablation that removes one or two ViT branches would isolate whether the multi-view fusion or the DINO pretraining is doing the heavy lifting; the paper does not report such an ablation.
  • A cheap testable extension is to use the surrogate as a denoiser or deconvolution prior for deeper, low-SNR z-layers, since reported errors remain stable with depth and reference SSIM actually improves for deeper stacks.
  • Applying the trained surrogate to perturbed tissues (RNAi, drug treatment) and checking whether its feature-space outputs shift in biologically expected directions would test whether the model captures tissue state rather than merely reproducing image statistics.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces the Vision Transformer Digital Twin Surrogate Network (VT-DTSN), a DINO-pretrained ViT with a multi-view crop/fusion scheme and a composite MSE + SSIM + cosine-similarity loss, intended to reconstruct or predict 3D+T Drosophila midgut imaging data. The model is evaluated on 18 z-stack layers across eight biological replicates, with average MSE ~9.33, SSIM ~0.871, and cosine similarity ~0.848. The authors claim that VT-DTSN learns high-fidelity, time-resolved tissue dynamics and acts as a digital-twin surrogate for in silico experimentation. The paper presents a conceptual framework, architecture details, and quantitative and qualitative results, with code made publicly available.

Significance. If the claims were fully supported, a ViT-based surrogate for 3D+T tissue imaging could be a useful tool for complementing live imaging and reducing experimental load. The authors deserve credit for using held-out biological replicates (no image-level leakage), for leveraging external DINO pretraining, and for releasing code and trained models. However, the central 'time-resolved dynamics' claim is not supported by the dataset design, as Section 3.2 explicitly states that each timepoint is an independent specimen with no repeated imaging of the same midgut. The reported metrics assess spatial reconstruction of static z-stacks, not prediction of future tissue states. The absence of baselines, ablations, and reported loss weights further weakens the evidence for the proposed architecture's advantages.

major comments (4)
  1. [Abstract, Section 3.2, Section 5] The central claim of learning 'high-fidelity, time-resolved dynamics' is contradicted by the data description. Section 3.2 states that 'time points correspond to different biological specimens and do not involve maintaining individual guts ex vivo for repeated imaging' and that 'There is no repeated imaging of the same midgut. Temporal therefore denotes cross-sectional dynamics across biological replicates rather than within-specimen time-lapse.' Section 5 likewise concedes that 'timepoints are cross-sectional across independent specimens.' With no specimen observed at more than one timepoint, there is no ground-truth temporal trajectory, and the evaluations in Table 1 and Figures 5–6 are spatial reconstruction accuracies on independent static z-stacks. The Abstract's 'time-resolved dynamics' and the 'digital twin surrogate' framing therefore overstate what the experiment demonstrates. T
  2. [Table 1, Section 4] No baseline comparisons are reported. The paper claims that CNNs fall short (Section 1, Section 3.3) and that DINO pretraining and multi-view fusion improve generalization, but Table 1 only reports absolute metrics for VT-DTSN. Without comparison to a trivial baseline (e.g., copying the input, nearest-neighbor, or mean image), a standard CNN (e.g., U-Net or ResNet-based), and a ViT without DINO pretraining, the reported MSE/SSIM/cosine values cannot establish that the model is a 'high-fidelity surrogate.' In particular, Table 1(b) shows a 'Reference SSIM' that is quite high, but no analogous reference MSE or cosine, so it is unclear how much of the apparent success is intrinsic to the data statistics. Adding baselines and statistical tests across the eight replicates is necessary to support the model's claimed advantages.
  3. [Eq. (1), Section 3.3.1, Section 3.3.4] The composite loss in Eq. (1) uses the same terms (MSE, SSIM, cosine similarity) as the evaluation metrics, and the loss weights alpha, beta, gamma are described as 'tuned' but not reported. This raises a circularity concern: optimizing exactly the quantities that are later reported as successes can inflate apparent performance. The authors should report the loss weights, ablate the influence of each term, and also evaluate with independent metrics, such as perceptual distance (LPIPS), segmentation Dice on marker-positive cells, or a downstream biological task, to demonstrate that the reconstructions are meaningful beyond optimization of the reporting criteria.
  4. [Section 3.3, Section 4] The claimed contributions of DINO pretraining, multi-view fusion, and ViT-based architecture are not tested with ablations. The paper states that 'three distinct ViTs' and 'multi-view fusion' are used, but there are no experiments removing DINO pretraining, replacing the three-branch fusion with a single ViT, or substituting a CNN backbone. Because the central claim is that this specific architecture is a feasible surrogate, the absence of ablations leaves the architecture choices unjustified. At minimum, the authors should compare against a single ViT, a CNN, and a non-DINO-initialized ViT to show that each design element contributes.
minor comments (5)
  1. [Throughout] The model name is inconsistent: 'VT-DTSN' in the title and abstract but 'VT-DTSM' in Sections 4 and 5 (e.g., 'VT-DTSM' in Section 4.1). Please standardize.
  2. [Section 1, 'imgaging'] Typo: 'time-resolved imgaging' should be 'imaging.' Also, 'cell culture image data' in Section 3.3 appears to be a mismatch, as the study uses Drosophila midgut tissue, not cell cultures.
  3. [Table 1(a)] MSE values are reported without normalization details. Since images are min-max normalized to [0,1], MSE values of 0.89–18.95 seem inconsistent unless the metric is computed on a different scale (e.g., 0–255). Please specify the exact normalization and MSE computation domain.
  4. [Figures 5 and 6] The axes and legends in the multi-panel figures are small and sometimes ambiguous (e.g., 'Frequency / Value' in Figure 6, and the violin/histogram panels lack clear label formatting). Larger fonts and explicit panel titles would improve readability.
  5. [Section 2] The literature review would benefit from a more systematic discussion of existing digital twin and surrogate modeling work for biological imaging, rather than a general survey of CNN/ViT limitations. This would better position the claimed novelty.

Circularity Check

2 steps flagged · score 6.0 of 10

The 'time-resolved dynamics' prediction is self-definitional: the data contain no within-specimen time series, and 'temporal' is redefined as cross-sectional variability, so VT-DTSN's outputs are a fitted cross-sectional image mapping rather than a digital-twin forecast; evaluation metrics also mirror the training loss.

  1. self definitional [Section 3.2 Data Collection and Preprocessing; Abstract]
    "Each timepoint corresponds to an independent specimen. There is no repeated imaging of the same midgut. ‘Temporal’ therefore denotes cross-sectional dynamics across biological replicates rather than within-specimen time-lapse."

    The abstract claims VT-DTSN 'learns to reconstruct high-fidelity, time-resolved dynamics of a Drosophila midgut,' but the methods define 'temporal' as cross-sectional variability between independent flies. Hence the target at t+Δt is not the future state of the same tissue; it is another specimen's static snapshot. The network is trained to map one individual's image to another individual's later-day image, so the 'prediction' is, by construction, a cross-sectional image-to-image translation rather than a forecast of tissue dynamics. The MSE/SSIM/cosine scores therefore measure agreement with a different biological sample, not temporal predictive accuracy, making the '3D+T digital twin' claim equivalent to a re-labeled static cross-sectional reconstruction.

  2. fitted input called prediction [Section 3.3.1 Custom Loss Function Formulation; Section 3.3.4 Evaluation Methodology]
    "We chose metrics mirroring our custom loss function, ensuring alignment between training and evaluation."

    The composite loss L = α·LMSE + β·LSSIM + γ·LCosine is exactly the set of metrics reported in Table 1 and Figure 5. The paper explicitly states the metrics 'mirror' the loss, so the reported 'low error rates and high structural similarity' are the direct training objective, not an independent confirmation of surrogate quality. The coefficients α, β, γ are said to be 'tuned' but their values are not reported, and no separate reference metric is provided. While held-out biological replicates give some generalization information, calling the trained-objective scores 'rigorous evaluation' elevates the fitting criterion to a validation claim.

full rationale

The paper is not circular through self-citation: the only self-citation is to the authors' GitHub repository [14], which is not load-bearing scientific evidence, and the DINO pretrained weights are external. However, the central claim of a '3D+T digital twin surrogate' that learns 'time-resolved dynamics' is constructively circular with respect to the data design. Section 3.2 explicitly states that days 4, 8, and 12 come from separate flies with no repeated imaging of the same midgut, and that 'temporal' denotes cross-sectional variability. Therefore the 'prediction' of tissue dynamics is defined in terms of cross-sectional differences between independent specimens. The model is fitted to map one static snapshot to another static snapshot from a different fly; no temporal trajectory exists to be learned or evaluated. The abstract and conclusion nevertheless present this as high-fidelity time-resolved dynamics, which reduces the 'prediction' to a re-labeled static cross-sectional reconstruction. Additionally, the evaluation metrics are the same functions used in the training loss, with the paper explicitly stating that they 'mirror' the loss, so the reported accuracy partly restates the training objective. These issues are partially mitigated by the honest limitation statement in Section 5 and by the use of held-out biological replicates, which provide some independent generalization evidence. Overall, the central temporal-surrogate claim is materially weakened by a self-definitional reduction, warranting a score of 6.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper does not introduce new physical entities; 'digital twin' is a framing of a statistical surrogate, not a new object. The central load-bearing assumptions are about feature transfer from DINO pretraining, normalization fidelity, the sufficiency of image-similarity metrics as biological proxies, and the independence of biological replicates.

free parameters (2)
  • Composite loss weights alpha, beta, gamma = not reported (tuned)
    Equation (1) combines MSE, SSIM, and cosine losses with coefficients that are tuned, but the paper never reports their values. These weights directly shape the trained model and the evaluation numbers.
  • Multi-view crop and fusion hyperparameters = 70% field-of-view, 20% overlap, Df=1024 (example)
    The crop sizes, overlap, and fusion dimension are hand-chosen architecture settings described in Section 3.3 and Figure 4. They affect how much context each ViT branch sees and how features are merged, though they are not fitted to a target metric.
assumptions (4)
  • domain assumption DINO-pretrained ViT features transfer to 3D+T confocal fluorescence microscopy without domain-specific pretraining.
    Section 3.3 selects 'vit base patch8 224 dino' pretrained on natural images and fine-tunes it on midgut confocal stacks. This assumes the self-supervised features are a useful starting point for cellular morphology.
  • domain assumption Per-image min-max normalization to [0,1] preserves biologically meaningful signal and is compatible with the loss metrics.
    Section 3.2 normalizes images to [0,1], but the reported MSE values are far above 1, suggesting either a different normalization or a metric computation inconsistency. The assumption is still load-bearing for interpreting all quantitative results.
  • domain assumption MSE, SSIM, and cosine similarity are sufficient proxies for biological fidelity and downstream usability.
    Section 3.3.1 designs the loss around these metrics and Section 4 uses the same metrics for evaluation. The paper provides no independent biological validation (e.g., segmentation accuracy, cell counting, or perturbation response) to confirm that high scores imply biologically meaningful reconstructions.
  • domain assumption Held-out biological replicates are independent samples from the same imaging process.
    Section 3.2 splits by entire replicates to prevent leakage. This assumes the eight flies are representative of the same underlying distribution and that no hidden batch effects dominate the results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics." pith.science (2026). https://pith.science/paper/GRXVEXX2

@misc{pith2026250815883,
  author       = {Pith},
  title        = {Pith review of: Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GRXVEXX2}},
  note         = {Machine review of arXiv:2508.15883}
}
read the original abstract

Understanding the dynamic organization and homeostasis of living tissues requires high-resolution, time-resolved imaging coupled with methods capable of extracting interpretable, predictive insights from complex datasets. Here, we present the Vision Transformer Digital Twin Surrogate Network (VT-DTSN), a deep learning framework for predictive modeling of 3D+T imaging data from biological tissue. By leveraging Vision Transformers pretrained with DINO (Self-Distillation with NO Labels) and employing a multi-view fusion strategy, VT-DTSN learns to reconstruct high-fidelity, time-resolved dynamics of a Drosophila midgut while preserving morphological and feature-level integrity across imaging depths. The model is trained with a composite loss prioritizing pixel-level accuracy, perceptual structure, and feature-space alignment, ensuring biologically meaningful outputs suitable for in silico experimentation and hypothesis testing. Evaluation across layers and biological replicates demonstrates VT-DTSN's robustness and consistency, achieving low error rates and high structural similarity while maintaining efficient inference through model optimization. This work establishes VT-DTSN as a feasible, high-fidelity surrogate for cross-timepoint reconstruction and for studying tissue dynamics, enabling computational exploration of cellular behaviors and homeostasis to complement time-resolved imaging studies in biological research.

Figures

Figures reproduced from arXiv: 2508.15883 by the authors.

Figure 1
Figure 1. Conceptual schematic illustrating the Vision Transformer Digital Twin Surrogate Network (VT-DTSN) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Zoomed view of the midgut region of interest. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The 18 Z-stack images creating the 3D representation. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: VT-DTSN architecture with view construction, ViT hyperparameters, fusion/reconstruction heads, and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Comprehensive quantitative evaluation of the Vision Transformer Digital Twin Surrogate Network (VT [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Scatter plots depicting the average values of the eight distinct sample sets for the Mean Squared Error [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Comparison of the ground truth and predicted images of the sample 1. From left to right: Ground Truth X [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 26 canonical work pages

  1. [1]

    Anatomy and physiology of the digestive tract of drosophila melanogaster,

    I. Miguel-Aliaga, H. Jasper, and B. Lemaitre, “Anatomy and physiology of the digestive tract of drosophila melanogaster,” Genetics, vol. 210, no. 2, pp. 357–396, 2018

    work page 2018

  2. [2]

    Intestinal stem cell response to injury: lessons from drosophila,

    H. Jiang, A. Tian, and J. Jiang, “Intestinal stem cell response to injury: lessons from drosophila,” Cellular and Molecular Life Sciences , vol. 73, no. 17, pp. 3337–3349, 2016. 10 XYRGBClass YClass Pred Figure 7: Comparison of the ground truth and predicted images of the sample 1. From left to right: Ground Truth X Value, Ground Truth Y Value, Predicted ...

    work page 2016

  3. [3]

    Intestinal stem cell function in drosophila and mice,

    H. Jiang and B. A. Edgar, “Intestinal stem cell function in drosophila and mice,” Current opinion in genetics & development, vol. 22, no. 4, pp. 354–360, 2012

    work page 2012

  4. [4]

    Deep-learning–enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology,

    G. Huang, Y. Hu, W. Lin, C. Shen, J. Yang, Z. Xie, Y. Ge, X. Jin, X. Qian, and M. Xu, “Deep-learning–enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology,” Journal of Biomedical Optics, vol. 30, no. 4, pp. 046 008–046 008, 2025

    work page 2025

  5. [5]

    Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models,

    C. A. Arledge, D. M. Sankepalle, W. N. Crowe, Y. Liu, L. Wang, and D. Zhao, “Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models,” Frontiers in bioscience (Landmark edition), vol. 27, no. 3, p. 99, 2022

    work page 2022

  6. [6]

    Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue,

    J. Hinrichsen, C. Ferlay, N. Reiter, and S. Budday, “Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue,” Frontiers in Physiology , vol. 15, p. 1321298, 2024

    work page 2024

  7. [7]

    Predicting 3d soft tissue dynamics from 2d imaging using physics informed neural networks,

    M. Movahhedi, X.-Y. Liu, B. Geng, C. Elemans, Q. Xue, J.-X. Wang, and X. Zheng, “Predicting 3d soft tissue dynamics from 2d imaging using physics informed neural networks,” Communications Biology, vol. 6, no. 1, p. 541, 2023

    work page 2023

  8. [8]

    Crnn-refined spatiotemporal transformer for dynamic mri reconstruction,

    B. Wang, Y. Lian, X. Xiong, H. Han, and Z. Liu, “Crnn-refined spatiotemporal transformer for dynamic mri reconstruction,” Computers in Biology and Medicine , vol. 182, p. 109133, 2024

    work page 2024

Show all 32 references
  1. [9]

    Self-supervision advances morphological profiling by unlocking powerful image representations,

    V. Kim, N. Adaloglou, M. Osterland, F. M. Morelli, M. Halawa, T. K¨ onig, D. Gnutt, and P. A. Marin Zap- ata, “Self-supervision advances morphological profiling by unlocking powerful image representations,” Scientific Reports, vol. 15, no. 1, p. 4876, 2025

    2025

  2. [10]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020

    2010 arXiv

  3. [11]

    Do vision transformers see like con- volutional neural networks?

    M. Raghu, T. Unterthiner, S. Kornblith, C. Zhang, and A. Dosovitskiy, “Do vision transformers see like con- volutional neural networks?” Advances in Neural Information Processing Systems , vol. 34, pp. 12 116–12 128, 2021

    2021

  4. [12]

    Pytorch image models,

    R. Wightman, “Pytorch image models,” https://github.com/huggingface/pytorch-image-models, 2019

    2019

  5. [13]

    Emerging properties in self- supervised vision transformers,

    M. Caron, H. Touvron, I. Misra, H. J´ egou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self- supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 9650–9660

    2021

  6. [14]

    VT-DTSN-Drosophila: Code and trained models,

    K. B. Ugurlar, J. de Navascu´ es, and M. T. Barros, “VT-DTSN-Drosophila: Code and trained models,” https: //github.com/kaanberke/vt-dtsn-drosophila, 2025, version v1.0.0 (commit #9f3a7c2), Accessed: Aug. 6, 2025

    2025

  7. [15]

    Quantization robust pruning with knowledge distillation,

    J. Kim, “Quantization robust pruning with knowledge distillation,” IEEE Access, vol. 11, pp. 26 419–26 426, 2023

    2023

  8. [16]

    Pqk: model compression via pruning, quantization, and knowledge distillation,

    J. Kim, S. Chang, and N. Kwak, “Pqk: model compression via pruning, quantization, and knowledge distillation,” arXiv preprint arXiv:2106.14681 , 2021

    2021 arXiv

  9. [17]

    Distributed deep learning inference acceleration using seamless collaboration in edge computing,

    N. Li, A. Iosifidis, and Q. Zhang, “Distributed deep learning inference acceleration using seamless collaboration in edge computing,” in ICC 2022-IEEE International Conference on Communications . IEEE, 2022, pp. 3667–3672

    2022

  10. [18]

    La- conic deep learning inference acceleration,

    S. Sharify, A. D. Lascorz, M. Mahmoud, M. Nikolic, K. Siu, D. M. Stuart, Z. Poulos, and A. Moshovos, “La- conic deep learning inference acceleration,” in Proceedings of the 46th International Symposium on Computer Architecture, 2019, pp. 304–317

    2019

  11. [19]

    Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models,

    J. Lamy-Poirier, “Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models,” arXiv preprint arXiv:2106.02679 , 2021

    2021 arXiv

  12. [20]

    End-to-end multiple instance learning with gradient accumulation,

    A. Andersson, N. Koriakina, N. Sladoje, and J. Lindblad, “End-to-end multiple instance learning with gradient accumulation,” in 2022 IEEE International Conference on Big Data (Big Data) . IEEE, 2022, pp. 2742–2746

    2022

  13. [21]

    Scheduling techniques for liver segmentation: Reducelronplateau vs onecyclelr,

    A. Al-Kababji, F. Bensaali, and S. P. Dakua, “Scheduling techniques for liver segmentation: Reducelronplateau vs onecyclelr,” in International Conference on Intelligent Systems and Pattern Recognition . Springer, 2022, pp. 204–212

    2022

  14. [22]

    Improving model selection by nonconvergent methods,

    W. Finnoff, F. Hergert, and H. G. Zimmermann, “Improving model selection by nonconvergent methods,” Neural Networks, vol. 6, no. 6, pp. 771–783, 1993. 12

    1993

  15. [23]

    Dropout: A simple way to prevent neural networks from overfitting,

    N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 56, pp. 1929–1958,

    1929

  16. [24]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

    2014 arXiv

  17. [25]

    Mean square error,

    M. D. Schluchter, “Mean square error,” Encyclopedia of Biostatistics , vol. 5, 2005

    2005

  18. [26]

    Mean squared error,

    J. F¨ urnkranz, P. Chan, S. Craw, C. Sammut, W. Uther, A. Ratnaparkhi, X. Jin, J. Han, Y. Yang, K. Morik et al. , “Mean squared error,” Encyclopedia of machine learning , 2010

    2010

  19. [27]

    Understanding ssim,

    J. Nilsson and T. Akenine-M¨ oller, “Understanding ssim,” arXiv preprint arXiv:2006.13846 , 2020

    2006 arXiv

  20. [28]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004

    2004

  21. [29]

    Structural similarity index (ssim) revisited: A data-driven approach,

    I. Bakurov, M. Buzzelli, R. Schettini, M. Castelli, and L. Vanneschi, “Structural similarity index (ssim) revisited: A data-driven approach,” Expert Systems with Applications , vol. 189, p. 116087, 2022

    2022

  22. [30]

    Structural similarity index family for image quality assessment in radiological images,

    G. P. Renieblas, A. T. Nogu´ es, A. M. Gonz´ alez, N. G´ omez-Leon, and E. G. Del Castillo, “Structural similarity index family for image quality assessment in radiological images,” Journal of medical imaging , vol. 4, no. 3, pp. 035 501–035 501, 2017

    2017

  23. [31]

    Learning similarity with cosine similarity ensemble,

    P. Xia, L. Zhang, and F. Li, “Learning similarity with cosine similarity ensemble,” Information sciences, vol. 307, pp. 39–52, 2015. 13

    2015

  24. [2014]

    Available: http://jmlr.org/papers/v15/srivastava14a.html

    [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.html

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.