Synthetic Image Generation

This task conjures visuals from scratch—think a fake “smiling face” for recognition or a crafted “X-ray” with a tumor (e.g., “street” with cars, all unreal)—to trick AI into learning better. Our team builds these fakes, cutting real-world data needs while boosting model chops.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are crucial in orchestrating the creation and refinement of data for Synthetic Image Generation within visual data workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce synthetic datasets that enhance AI’s performance across diverse vision tasks.

Training and Onboarding

PMs design and implement training programs to ensure workers master synthetic design, realism tuning, and scenario crafting. For example, they might train teams to generate “crowded mall” scenes or “fractured bone” scans, guided by sample renders and realism benchmarks. Onboarding includes hands-on tasks like creating synthetic images, feedback loops, and calibration sessions to align outputs with AI training goals. PMs also establish workflows, such as multi-step reviews for lifelike quality.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., generating 20,000 synthetic images) and set metrics like visual fidelity, diversity, or model improvement. They track progress via dashboards, address realism gaps, and refine methods based on worker insights or evolving synthetic needs.

Collaboration with AI Teams

PMs connect creators with machine learning engineers, translating technical requirements (e.g., varied lighting for faces) into actionable generation tasks. They also manage timelines, ensuring synthetic datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The generators, designers, or synthetic analysts perform the detailed work of crafting and refining synthetic image datasets for AI training. Their efforts are creative and technical, requiring artistry and data savvy.

Labeling and Tagging

For synthetic data, we might tag images as “generated face” or “mock CT.” In complex tasks, they label features like “shadow effect” or “synthetic crowd.”

Contextual Analysis

Our team builds scenes, crafting “rainy road” with “cars” or “skin lesion” on “arm,” ensuring AI trains on believable, varied fakes.

Flagging Violations

Workers review datasets, flagging flaws (e.g., “unreal blur”) or redundancies (e.g., identical fakes), maintaining dataset quality and utility.

Edge Case Resolution

We tackle complex cases—like rare synthetic anomalies or niche scenarios—often requiring custom tweaks or escalation to generation experts.

We can quickly adapt to and operate within our clients’ visual data platforms, such as proprietary synthetic tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of images per shift, depending on the complexity of the generation and images.

Data Volumes Needed to Improve AI

The volume of synthetic image data required to enhance AI systems varies based on the diversity of scenarios and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional synthetic model might require 5,000–20,000 generated images per category (e.g., 20,000 fake faces). For broad or specialized tasks, this could rise to ensure coverage.

Iterative Refinement

To boost performance (e.g., from 85% to 95%), an additional 3,000–10,000 images per issue (e.g., weak realism) are often needed. For instance, refining a model might demand 5,000 new synthetics.

Scale for Robustness

Large-scale applications (e.g., gaming AI) require datasets in the hundreds of thousands to handle edge cases, rare fakes, or new styles. A generation effort might start with 100,000 images, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags weak synthetics for further generation. This reduces total volume but requires ongoing effort—perhaps 500–2,000 images weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and synthetic precision across datasets.

Multilingual & Multicultural Synthetic Image Generation

We can assist you with synthetic image generation across diverse linguistic and cultural landscapes.

Our team is equipped to create and refine synthetic image data from global contexts, ensuring realistic, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: