Deep learning has rapidly advanced state-of-the-art results across image, text, speech and predictive analytics tasks in recent years. However, deep learning models rely heavily on massive datasets to build accuracy through discerning complex data patterns. Sourcing adequate data continues to be a key obstacle for applying deep learning more broadly.
This is where synthetic data holds great promise. By algorithmically generating simulated data with similar properties as real-world data, synthetic data can fulfill the hunger for data that deep learning thrives on. Let‘s analyze the pivotal role synthetic data is positioned to play in furthering deep learning capabilities.
Synthetic Data Helps Tackles Critical Challenges
Here we look at 3 fundamental challenges facing deep learning that synthetic data is well equipped to address:
1. Alleviate Data Scarcity Bottlenecks
The data demands of deep learning are immense thanks to the intricate machine-driven feature learning that underpins performance lift. As per estimates, AI models double in size every 3.4 months requiring 2x-5x more data for training. Self-driving vehicle datasets easily run into petabytes while popular ImageNet contains over 14 million categorized images.
Sourcing real-world data at such scale remains the top barrier according to 56% of surveyed enterprises below:
Smartly generated synthetic data preserves the statistical essence of real datasets while avoiding the drudgery of manual data collection or licensing costs of data marketplaces. Data can readily be produced on demand to feed deep learning needs.
2. Overcome Privacy & Compliance Restrictions
With personal data protection regulations like GDPR and HIPAA, many impactful deep learning use cases are non-starters due to the confidential nature of underlying real-world datasets. Healthcare, telecommunications and banking data can rarely be shared or require extensive data masking.
Synthetic equivalents containing no personal identifiers open up AI modeling previously considered impossible in these regulated domains. For instance, Synthea provides free synthetic patient records that look real without compromising sensitive medical data.
Such synthetic health data can serve as input for deep learning breakthroughs in areas ranging from predictive diagnoses to precision medicine recommendations.
3. Ease Supervised Learning with Labeled Data
Deep learning approaches like CNNs and RNNs rely on extensive labeled datasets where each data point is tagged with target variable values the model must predict. Manually labeling images, speech segments, genomic sequences or sensor logs at scale requires prohibitive human time and effort.
Synthesized data offers a shortcut where datapoints are auto-labeled during generation allowing supervision without human involvement. Waymo uses simulated self-driving data with automatic scene labels for categories like vehicles, pedestrians and traffic lights. This powers deep learning algorithms behind autonomous vehicle perception and planning.
Other domains with sparse expert labeling resources can immensely leverage synthetically generated supervised data for more extensive deep learning.
High Impact Application Areas
Let‘s analyze how synthetic data adoption is enabling step function improvements across diverse deep learning applications:
Healthcare
In medical imaging tasks, less than 5% of rare conditions have enough representative training data available. Pioneering approaches like Karolinska Institute improved anomaly detection in retina scans using synthetic minority oversampling. Defect classification accuracy jumped from 0.63 to 0.82 by training on augmented datasets.
Such synthetic data techniques can unlock deep learning to boost everything from accurate diagnoses to early disease warnings across specialities. Market research predicts the healthcare synthetic data market to clock nearly 50% CAGR reaching $193 million by 2026.
Finance
Fraudulent transactions are only a tiny portion making up 0.1% of total payment data. This extreme imbalance makes reliable deep learning-based fraud detection challenging.
Mastercard boosted detection rates by 15% blending real and synthetic fraud data generated based on danger patterns and red flags. Similarly, a Singapore bank improved detection by 8% with an LSTM model trained on enriched synthetic data. As deep learning picks up steam for security, synthetic data is pivotal.
Autonomous Vehicles
Alphabet subsidiary Waymo uses high-fidelity simulated driving data for training perception systems of its self-driving cars. Built via its Carcraft system, synthetic data across dense urban environments and rare edge cases fills gaps unfeasible through real-world test runs.
Deep learning that powers visual sensemaking and behavioral prediction for autonomous vehicles heavily utilizes synthetically generated miles. The automated data manufacturing methodology developed by Waymo was so successful, they spun it out into an offering called Waymo Open Dataset.
Advanced simulation systems paired with deep generative methods will continue to widen adoption beyond Waymo.
Robotics
For notion-intensive domains like robotics, creating datasets through physical rigs and test runs is operationally complex. Synthetic data generated automatically through digital twins offers a handy shortcut.
Toyota Research Institute trained manipulation tasks on synthetic data improving success rates by 50-70% using just 10% of real samples compared to a model trained purely on observed data. Data-efficient synthesis gives robotics a sorely needed kickstart.
Latest Advances in Deep Generative Techniques
What is fueling the momentum behind synthetic data? The answer lies in remarkable progress with deep generative adversarial networks (GANs) and variational autoencoders (VAEs) suitable for unsupervised machine learning tasks. Let‘s analyze some leading techniques:
High-Fidelity Image Generation using StyleGAN
Nvidia research on StyleGAN push state-of-the-art results in photorealistic image synthesis using an ingeniously redesigned GAN architecture. By disentangling high-level attributes and stochastic variation in the generated output, StyleGAN better models dataset distribution enabling both sample quality and diversity.
The outcomes auto-labeled with scene descriptions or object bounding boxes unlock new deep learning horizons across computer vision.
Synthesizing Medical Time-Series Data
Generative Replay combines VAEs with Monte Carlo dropout sampling for robust time series generation. Tested on real ICU records, it creates high-fidelity synthetic data preserves trends, seasonality, and dependencies found in source sequences.
Such credible medical time-series synthesis eludes previous RNN-based approaches and opens up new possibilities for patient trajectory analysis and intervention recommenders.
Capturing Biological Signal Dynamics
Researchers combined GANs, physics simulators and fuzzy logic to generate synthetic multi-channel time series with intricate real-world dynamics. Tested on ion channel recordings, the proposed NeuRoSyn model catches complex latent behaviors missed by mainstream generative models.
With such scientific data synthesis capabilities, significant new frontiers will open up including bioinformatics, neuroscience and internet of things.
As tools like Tensorflow GAN libraries and Azure Machine Learning‘s synthetic data modules advance, practitioners can stand on the shoulders of groundbreaking research to tap the power of synthetic data.
Production Challenges with Synthetic Data
However, some open challenges remain around integrating synthetic data into applied model building:
1. Benchmarking and Monitoring Data Quality
Like any raw material, synthetic data quality directly impacts downstream model performance. Quantitatively measuring fidelity aspects like label accuracy, statistical variance, seasonality patterns, outlier ratios and cluster densities is key.
Human-in-the-loop testing on smaller samples also offers quality assurance while production systems tune generator algorithms.
2. Checking and Mitigating Unwanted Bias
Source datasets often encode biases around gender, race or socio-economic divides that get implicitly magnified through synthesis. Proactively testing for uneven error rates, dropout impacts across user groups and stereotypical distortions helps catch issues early.
Adopting techniques like adversarial debiasing and distribution-preserving data augmentation further improve model robustness and fairness.
3. Validating Performance on Real Data
While synthetic data powers the model building phase, real-world validation on holdout datasets indicates actual in-the-field viability. Monitoring metrics like data drift helps keep track of deviation between production data patterns and employed synthetic data.
Techniques like A/B testing with model variants trained on synthetic vs real datasets provide instructive insights into generalization capability.
Investing into the above rigorous checks and monitoring as part of internal development workflows pays off manifold downstream.
Industry Outlook on Synthetic Data
Leading research and advisory firms paint an optimistic picture on increasing synthetic data adoption:
- Gartner: 60% of data for AI projects to be synthetically generated by 2024
- MarketsandMarkets: $1.6 billion synthetic data market size by 2026
- Grand View Research: 58% annualized growth rate between 2022-2030
This growth will likely accelerate as data-centric AI continues crossing chasms across verticals through relentless democratization and cloudification.
On the startup front, synthetic data ISVs like MostlyAI, DataGen, AI.Reverie, and BlueSaber are offering various data-as-a-service offerings to meet this demand explosion. Larger platforms have also jumped in – AWS with its Synthetic Data Vault and Databricks‘ ML runtime Databricks‘ ML runtime Databricks‘ ML runtime Databricks‘ ML runtime Databricks‘ ML runtime for Teams
As best practices evolve, not investing into synthetic data puts enterprises at risk of losing out on harnessing the next AI wave. The opportunity can catalyze those devising visionary analytics strategies leveraging synthetic data‘s might.