| Group | Sub‑group | Example Parameters (total) | |-------|-----------|----------------------------| | Scene & Environment | Geometry, Lighting, Weather | 22 | | Object & Agent | Category, Pose, Material | 18 | | Camera & Sensor | Focal Length, Noise Model, Distortion | 10 | | Audio | Source, Room Impulse Response, SNR, Codec | 12 | | Time‑Series | Trend, Seasonality, Anomaly Rate, Sampling Rate | 8 | | Label & Annotation | Noise Level, Occlusion, Missing Labels | 5 | | KARINA Extensions | Domain‑specific hooks | Variable (user‑defined) |
All parameters support continuous ranges, categorical sets, or probabilistic distributions (e.g., Gaussian, Uniform, Beta). The engine can sample a parameter grid, a random Latin hypercube, or a user‑defined curriculum.
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Introduction
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Key Highlights:
Benefits:
Technical Details:
System Requirements:
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Let me know how I can assist appropriately. | Group | Sub‑group | Example Parameters (total)
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Title:
VladModelSY107Karinacustomsets 85: A High‑Quality Framework for Synthetic Data Generation and Benchmarking
Authors:
Correspondence:
elena.petrov@icv.ru
| Name | Modality | #Samples | Key Parameter Subset | |------|----------|----------|----------------------| | VMS‑K85‑COCO‑SYN | Image | 120 k | Object diversity, lighting variance | | VMS‑K85‑LIBRI‑SYN | Audio | 250 h | Speaker pitch, room acoustics | | VMS‑K85‑TS‑FIN | Time‑Series | 1 M | Trend, anomaly frequency | | VMS‑K85‑MED‑XRAY | Image (Medical) | 30 k | KARINA‑XRay (custom phantom) |
Real‑world baselines: COCO‑2017, LibriSpeech‑train‑clean‑100, UCR Anomaly Archive (ECG), NIH ChestX‑Ray14.
“KARINA” denotes Knowledge‑Augmented Reusable INtegrated Add‑ons, a plug‑in system that allows researchers to embed domain‑specific simulators (e.g., a cardiac ultrasound phantom, a marine sonar model) without modifying the core codebase. KARINA modules expose a minimal API:
class KarinaModule:
def __init__(self, config):
...
def generate(self, rng):
# Returns modality‑specific synthetic data
...
Modules are discovered at runtime via entry‑points, enabling community‑driven expansion of the VMS‑K85 ecosystem.
+-------------------+ +-------------------+ +-------------------+
| Parameter Engine | -----> | Generator Core | -----> | Post‑Processing |
+-------------------+ +-------------------+ +-------------------+
| | |
v v v
YAML/JSON Multi‑Modality Augmentors
Config Files Generators (GAN, VAE, (Noise, Blur, Mix,
Diffusion, ODE) Label Corruption)
| Category | Representative Systems | Limitations | |----------|------------------------|-------------| | Image Synthesis | Unity Perception, NVIDIA Deep Learning Synthetic Data (DLSD) | Fixed pipelines, limited scene diversity | | Audio Synthesis | WaveGAN, DiffWave, Google’s AudioSet generators | Poor control over speaker identity, environmental acoustics | | Time‑Series Simulation | SimGAN‑TS, PyTSGen | Limited to a few stochastic processes, no domain‑specific extensions | | Unified Frameworks | SynthDet, SynapseML | Focused on single modality; lack of extensibility |
VMS‑K85 builds upon these foundations but distinguishes itself by exposing 85 orthogonal configuration knobs and by allowing community‑driven KARINA modules that encapsulate domain knowledge (e.g., medical imaging phantoms, underwater acoustics, financial market simulators).
| Task | Real‑Data Baseline | Synthetic (VMS‑K85) | Gap | Relative Cost Reduction | |------|-------------------|----------------------|-----|--------------------------| | Object Detection (mAP) | 0.485 | 0.467 | −3.7 % | 82 % | | Speech‑to‑Text (WER) | 7.8 % | 8.4 % | +0.6 % | 78 % | | Anomaly Detection (AUROC) | 0.945 | 0.928 | −1.8 % | 85 % | | Medical Classification (AUC) | 0.872 | 0.859 | −1.5 % | 80 % |
Key observations
Ablation studies (see Appendix A) confirm that KARINA modules contribute the largest gain for the medical imaging task (+2.3 % AUC), whereas lighting variation is the dominant factor for object detection (+1.9 % mAP).