cs.LGFeb 26, 2026

A Dataset is Worth 1 MB

Authors: Elad Kimchi Shoshani, Leeyam Gabay, Yedid Hoshen

Organizations: School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel.

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Abstract

A dataset server must often distribute the same large payload to many clients, incurring massive communication costs. Since clients frequently operate on diverse hardware and software frameworks, transmitting a pre-trained model is often infeasible; instead, agents require raw data to train their own task-specific models locally. While dataset distillation attempts to compress training signals, current methods struggle to scale to high-resolution data and rarely achieve sufficiently small files. In this paper, we propose Pseudo-Labels as Data (PLADA), a method that completely eliminates pixel transmission. We assume agents are preloaded with a large, generic, unlabeled reference dataset (e.g., ImageNet-1K, ImageNet-21K) and communicate a new task by transmitting only the class labels for specific images. To address the distribution mismatch between the reference and target datasets, we introduce a pruning mechanism that filters the reference dataset to retain only the labels of the most semantically relevant images for the target task. This selection process simultaneously maximizes training efficiency and minimizes transmission payload. Experiments on 10 diverse datasets demonstrate that our approach can transfer task knowledge with a payload of less than 1 MB while retaining high classification accuracy, offering a promising solution for efficient dataset serving.

Figures & tables

Figure 1: Motivation. A dataset server transmits the same large dataset many times at massive cost. Our method allows the server to send a compressed payload of less than 1 MB, enabling clients with heterogeneous hardware, even if they have ultra-narrow bandwidth, to train their own models locally.
Figure 2: The PLADA Pipeline. The server (left) trains a teacher classifier on the task dataset and distills this task knowledge into hard labels on the reference data. It then filters to the lowest-uncertainty p%p\% of pseudo-labels and transmits a compressed payload ( <1<1 MB). The client (right) reconstructs a virtual dataset using its preloaded reference dataset and the payload to train the student model.
Figure 3: Reference set images vs. energy percentile. High-confidence (low-energy) samples retrieved from ImageNet-21K demonstrate semantic and structural alignment with the target domains. For additional visualizations see Appendix C .
Figure 4: Class distribution of the RESISC45 pseudo hard-labels , before and after filtering using safety-net. The yellow bars show the original global distribution, which is heavily imbalanced - RESISC45 has images extracted using Google Earth, out of which class 0 is airplane . Standard global filtering would eliminate some of the tail classes entirely. The blue bars demonstrate our Safety-Net Filtering (keeping 5%, α=−0.2\alpha=-0.2 ), which effectively preserves a representation of under-represented classes even under extreme compression. Note that the Y-axis uses a cube-root scale to visually accommodate the large magnitude differences between the ‘strong‘ and ‘weak‘ classes.
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
Dataset1%5%10%25%50%100%*1%5%10%25%50%100%*
Caltech-10179.84%88.94%90.21%90.73%92.45%92.74%66.59%77.13%82.49%86.35%86.52%87.50%
CIFAR-1063.31%85.31%88.12%91.68%92.62%86.13%53.75%72.02%74.83%79.90%84.96%87.66%
CUB-20082.49%82.36%82.44%81.34%81.09%74.94%22.94%45.89%51.40%56.19%55.60%52.97%
DTD66.65%70.16%70.69%70.80%68.83%68.14%52.45%58.99%60.48%60.90%61.97%62.29%
FGVC-Aircraft53.62%45.51%46.41%45.87%43.53%32.16%20.04%23.91%26.58%29.16%30.18%29.40%
Table 1: Keep rate evaluations. Student accuracy when trained on the top- pp images of the reference set, according to logit Energy. We achieve accurate classification on target tasks using only a fraction of the reference set.
  Using 100 ImagesUsing 500 Images 
DatasetOurs (p=1%)RandomK-CentersRandomK-CentersDD †
Caltech-10186.69%32.78%34.16%47.98%51.04%–
CIFAR-1076.75%28.66%19.33%31.29%27.20%73.2%
CUB-20082.49%4.58%3.69%9.67%7.55%16.2%
DTD68.09%19.04%14.73%36.49%28.35%–
FGVC-Aircraft53.62%2.76%2.10%4.62%4.59%–
Table 2: Baseline comparison. We compare student accuracy across 10 benchmarks. Our method, PLADA (using 1% keep ratio with possible Safety-Net filtering on ImageNet-21K, see Table 3 ), outperforms data-transmission baselines—including random sampling, K-Center coresets, and Dataset Distillation (DD)—in both accuracy and payload size. Notably, PLADA achieves superior task recovery while requiring a payload significantly smaller than even the aggressive 100-image JPEG-compressed subsets.
Dataset1% Vanil.1% Oppos.1%+Safe ( α=0.5\alpha=0.5 )1%+Safe ( α=−0.2\alpha=-0.2 )
Caltech-10179.84%74.42%86.69%86.29%
CIFAR-1063.31%54.80%76.75%74.62%
CUB-20082.49%6.19%81.21%80.53%
DTD66.65%26.12%66.70%68.09%
FGVC-Aircraft53.62%3.45%43.23%44.58%
Food-10175.50%6.28%70.91%71.66%
Table 3: Impact of safety-net filtering on Student Accuracy. All 1% subsets are sampled from ImageNet-21K. We compare the default lowest-energy filtering and its counterpart (highest-energy filtering) against Safety-Net variants. Low-energy samples (Vanilla) outperform high-energy ones. Safety-Net often further improves accuracy by preventing class collapse, with the same payload budget. The difference is the highest for RESISC45 (cf. Figure 4 ). Additional results are reported in Table 10 .
ppRaw SizeHuffmanZstd
0.5%0.41–1.83 MB77–305 KB45–109 KB
1%0.81–1.96 MB151–396 KB85–206 KB
5%3.05 MB0.57–1.10 MB0.40–0.88 MB
10%4.40–8.12 MB0.88–1.95 MB0.67–1.58 MB
25%8.46 MB1.65–4.34 MB1.21–3.47 MB
50%15.23–40.62 MB2.49–7.88 MB1.87–6.42 MB
Table 4: Payload size analysis. We compare across keep ratios ( pp ) and compression schemes. Ranges represent the minimum and maximum sizes observed across all 10 different datasets, and across different filtering options (with and without safety-net), with ImageNet-21K as the reference dataset. Raw indicates uncompressed fixed-width binary storage. Zstd represents the final compressed payload size using differential encoding and Zstandard compression (level 19). Full compression experiments results are provided in Appendix E .
Dataset1% Vanil.1% Oppos.1%+Safe ( α=0.5\alpha=0.5 )1%+Safe ( α=−0.2\alpha=-0.2 )
BloodMNIST18.24%59.28%47.00%41.45%
DermaMNIST53.32%67.68%47.58%38.05%
RetinaMNIST56.50%56.75%55.25%55.00%
NCT-CRC-HE18.69%43.51%32.57%32.37%
Table 5: Results on medical datasets † . ImageNet-21K ref. set.
Figure 5: Bandwidth-Accuracy Baselines (CUB-200). Comparison of PLADA against weight and data transmission baselines. PLADA (red star) dominates the top-left corner, achieving higher accuracy than weight-based methods while requiring a smaller payload (¡35 KB). Data-centric baselines (Random Subset/K-Center) fail to provide a viable signal at this extreme budget. All payloads are Zstd-compressed (level 19).
Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Target DatasetTotal IntersectionTest Intersection
Oxford Flowers 1020 / 8,1890 / 6,149
Food-1010 / 101,0000 / 25,250
DTD0 / 3,7600 / 1,880
CIFAR-100 / 60,0000 / 10,000
RESISC450 / 31,5000 / 6,300
Places3650 / 1,839,9600 / 36,500
Appendix
Table 6: Intersection analysis between Target Datasets and ImageNet-21K (14.2M). Columns show the number of duplicates found in the full target dataset ( x/nallx/n_{all} ) and the specific test set ( y/ntesty/n_{test} ).
 1% Filter5% Filter10% Filter25% Filter
DatasetEntropyEnergyEntropyEnergyEntropyEnergyEntropyEnergy
Caltech-10175.06%79.84%84.22%88.94%87.56%90.21%91.71%90.73%
CIFAR-1072.57%63.31%86.00%85.31%89.14%88.12%91.14%91.68%
CUB-20077.44%82.49%81.42%82.36%81.76%82.44%81.59%81.34%
DTD67.02%66.65%69.79%70.16%71.22%70.69%71.06%70.80%
FGVC-Aircraft37.32%53.62%44.13%45.51%45.69%46.41%45.90%45.87%
Appendix
Table 7: Entropy vs Energy Pruning. This table reports student accuracy † under different filtering budgets, comparing entropy-based pruning with energy-based pruning (ImageNet-21K).
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
Dataset1%5%10%1%5%10%25%
Caltech-10174.42%85.66%87.27%61.29%79.32%82.89%85.54%
CIFAR-1054.80%64.45%70.12%34.90%45.84%58.46%67.09%
CUB-2006.19%11.79%15.48%3.22%6.66%8.78%14.16%
DTD26.12%37.34%42.77%19.41%27.45%35.90%40.00%
FGVC-Aircraft3.45%5.37%8.16%2.46%3.57%4.74%8.70%
Appendix
Table 8: Opposite Energy-Based Pruning. This table shows the student accuracy obtained by training on the highest - p%p\% energy-score images (which are usually the ”worst” images), using ImageNet-1K and ImageNet-21K as reference datasets.
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
Dataset1%5%1%5%
BloodMNIST59.28%57.88%32.65%38.03%
DermaMNIST67.68%66.58%66.43%67.33%
RetinaMNIST56.75%58.00%50.25%55.50%
NCT-CRC-HE-100K43.51%52.48%26.46%35.33%
Appendix
Table 9: Medical Datasets Opposite Energy-Based Pruning. This table presents student accuracy obtained by training on the highest p%p\% energy-score images of the reference sets. As explained in Section 5.3 , we unexpectedly observed higher student accuracy when applying the opposite filtering strategy on the medical datasets.
 ImageNet-21KImageNet-1K
 1%5%1%5%10%25%
Datasetα ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5
Caltech-10186.29%86.69%90.67%91.07%77.25%77.25%83.41%83.81%86.81%85.20%86.06%85.83%
CIFAR-1074.62%76.75%83.83%85.44%58.07%58.59%69.97%68.27%73.51%72.56%77.00%78.26%
CUB-20080.53%81.21%82.06%82.06%15.48%15.78%41.26%39.61%52.54%52.50%48.39%49.15%
DTD68.09%66.70%70.59%70.48%53.62%51.65%60.16%60.11%61.81%62.29%61.28%62.77%
Appendix
Table 10: Safety-net Filtering. This table shows student accuracy when using Safety-Net filtering with α=−0.2,0.5\alpha=-0.2,0.5 across different filtering keep ratios, with ImageNet-21K and ImageNet-1K as reference sets.
 ImageNet-21KImageNet-1K
 1%5%1%5%
Datasetα ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5α ⁣= ⁣−0.2\alpha\!=\!-0.2α ⁣= ⁣0.5\alpha\!=\!0.5
BloodMNIST41.45%47.00%56.71%45.19%38.91%42.03%45.31%48.44%
DermaMNIST38.05%47.58%68.33%68.33%51.67%53.42%57.06%57.91%
NCT-CRC-HE-100K32.37%32.57%41.89%37.54%23.79%28.23%33.88%39.47%
RetinaMNIST55.00%55.25%63.25%61.25%53.75%55.75%58.50%58.25%
Appendix
Table 11: Medical Datasets Safety-net Filtering. This table shows student accuracy when using Safety-Net filtering with α=−0.2,0.5\alpha=-0.2,0.5 across different filtering keep ratios, with ImageNet-21K and ImageNet-1K as reference sets.
Figure 6: Energy percentiles on ImageNet-1K. For three target tasks (rows: Oxford-Flowers102, DTD, FGVC-Aircraft), we score every ImageNet-1K image using the corresponding target teacher and sort the reference set by logit energy ( Equation 5 ; lower is better). We then show exemplar reference images at fixed energy percentiles (columns). The horizontal bar under each row visualizes the full energy range over the entire reference set (dashed ticks mark the sampled percentiles). As the percentile increases (left →\rightarrow right), samples transition from target-aligned content (flowers/texture patterns/aircraft) to increasingly irrelevant images.
Figure 7: Energy percentiles on ImageNet-21K. Same visualization as Figure 6 but using ImageNet-21K (14.2M images) as the reference set. The larger and more diverse dataset typically provides closer semantic neighbors in the low-energy tail (e.g., more flower varieties and texture-like patterns).
Figure 8: Low-energy reference images (ImageNet-1K). For each target teacher (rows; top-to-bottom: Caltech-101, CIFAR-10, CUB-200, DTD, FGVC-Aircraft, Food-101, Oxford-Flowers, Oxford-Pets, Places365, RESISC45), we show ImageNet-1K reference images drawn from increasingly larger low-energy percentiles (columns: 0.0001%–1.5%). The extreme low-energy tail tends to contain canonical instances of the target concepts (e.g., birds for CUB-200, food dishes for Food-101, flowers for Oxford-Flowers).
Figure 9: Low-energy reference images (ImageNet-21K). Same as Figure 8 but using ImageNet-21K as the reference set. The larger reference set yields a richer and often more semantically aligned set of low-energy exemplars across targets, consistent with the higher student accuracy obtained with ImageNet-21K as the reference set in Table 1 .
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
ppRawCompactHuffmanZstdRawCompactHuffmanZstd
0.1% (Idx)83.19 KB69.32 KB21.66 KB20.95 KB7.51 KB6.25 KB1.57 KB1.73 KB
0.1% (Bmp)1.72 MB1.71 MB230.84 KB21.61 KB158.89 KB157.64 KB20.71 KB1.62 KB
0.5% (Idx)415.93 KB346.61 KB106.16 KB95.39 KB37.53 KB18.76 KB8.63 KB6.77 KB
0.5% (Bmp)1.83 MB1.76 MB289.91 KB84.14 KB168.90 KB162.65 KB26.05 KB6.52 KB
1% (Idx)831.86 KB415.93 KB200.09 KB151.80 KB75.06 KB37.53 KB16.86 KB12.59 KB
Appendix
Table 12: Compression Summary for Caltech-101, CIFAR-10, CUB-200-2011. In bold we highlight the best compression for every 2 rows (as we also compare between bitmap and delta indices encodings for the pruning bit).
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
ppRawCompactHuffmanZstdRawCompactHuffmanZstd
0.1% (Idx)83.19 KB69.32 KB21.10 KB22.48 KB7.51 KB3.75 KB1.46 KB1.61 KB
0.1% (Bmp)1.72 MB1.71 MB230.01 KB23.76 KB158.89 KB157.64 KB20.55 KB1.91 KB
0.5% (Idx)415.93 KB207.96 KB102.81 KB91.67 KB37.53 KB18.76 KB7.94 KB7.50 KB
0.5% (Bmp)1.83 MB1.76 MB286.34 KB95.17 KB168.90 KB162.65 KB25.03 KB7.47 KB
1% (Idx)831.86 KB415.93 KB197.48 KB175.57 KB75.06 KB37.53 KB15.83 KB14.57 KB
Appendix
Table 13: Compression Summary for DTD, FGVC-AIRCRAFT, FOOD-101
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
ppRawCompactHuffmanZstdRawCompactHuffmanZstd
0.1% (Idx)83.19 KB69.32 KB18.69 KB15.95 KB7.51 KB6.25 KB0.96 KB1.32 KB
0.1% (Bmp)1.72 MB1.71 MB231.20 KB14.31 KB158.89 KB157.64 KB20.37 KB1.13 KB
0.5% (Idx)415.93 KB346.61 KB87.85 KB60.86 KB37.53 KB18.76 KB7.23 KB6.80 KB
0.5% (Bmp)1.83 MB1.76 MB292.83 KB51.47 KB168.90 KB162.65 KB24.94 KB7.07 KB
1% (Idx)831.86 KB693.21 KB168.27 KB107.58 KB75.06 KB37.53 KB16.24 KB14.81 KB
Appendix
Table 14: Compression Summary for Oxford-Flowers102, Oxford-IIIT-Pet, Places365. Note that compression is less effective on Places365 due to its larger number of classes and reduced redundancy.
 ImageNet-21K (14.2M images)ImageNet-1K (1.2M images)
ppRawCompactHuffmanZstdRawCompactHuffmanZstd
0.1% (Idx)83.19 KB41.59 KB19.83 KB22.82 KB7.51 KB3.75 KB1.79 KB2.35 KB
0.1% (Bmp)1.72 MB1.71 MB226.58 KB25.10 KB158.89 KB157.64 KB20.44 KB3.03 KB
0.5% (Idx)415.93 KB207.96 KB87.16 KB94.19 KB37.53 KB18.76 KB8.58 KB9.92 KB
0.5% (Bmp)1.83 MB1.76 MB270.34 KB91.43 KB168.90 KB162.65 KB24.27 KB10.31 KB
1% (Idx)831.86 KB415.93 KB162.79 KB172.20 KB75.06 KB37.53 KB16.18 KB18.20 KB
Appendix
Table 15: Compression Summary for RESISC45
ppRawCompactHuffmanZstd
0.1%7.51–158.89 KB3.75–157.64 KB1.46–20.71 KB1.08–2.53 KB
0.5%37.53–168.90 KB18.76–168.90 KB7.23–26.28 KB3.15–9.92 KB
1%181.41 KB168.90–181.41 KB28.34–33.70 KB7.86–17.95 KB
5%281.51 KB218.95–281.51 KB68.02–98.31 KB35.13–77.31 KB
10%406.62 KB281.51–406.62 KB110.48–179.47 KB67.80–142.45 KB
25%0.76–1.83 MB469.18–781.96 KB223.66–403.47 KB149.08–314.62 KB
Appendix
Table 16: Compression Summary Over All Datasets: ImageNet-1K (1.2M images). This table summarizes the compression results over all of the datasets for all pp values. The values in the table are min-max sizes.
ppRawCompactHuffmanZstd
0.1%0.08–1.72 MB0.04–1.71 MB17.43–233.01 KB14.07–27.13 KB
0.5%0.41–1.83 MB0.20–1.83 MB77.75–305.21 KB45.48–108.84 KB
1%0.81–1.96 MB0.41–1.96 MB151.00–396.44 KB84.83–206.08 KB
5%3.05 MB2.37–3.05 MB0.57–1.10 MB401.63–877.05 KB
10%4.40–8.12 MB2.71–4.40 MB0.88–1.95 MB0.67–1.58 MB
25%8.46 MB5.08–8.46 MB1.65–4.34 MB1.21–3.47 MB
Appendix
Table 17: Compression Summary Over All Datasets: ImageNet-21K (14.2M images). In comparison to Table 16 , the payloads are roughly 10-12x larger.
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