Method and system for generating one or more conditionally dependent data entries
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Solution Overview
Problem
Existing methods struggle to efficiently generate long-range coherent and photo-realistic videos due to resource constraints and unrealistic conditional independence assumptions in current generative models, particularly in the context of deep learning and recurrent neural networks.
Innovation Solution
A probabilistic generative model, specifically a score-based diffusion model like denoising diffusion probabilistic model (DDPM), is trained using a flexible architecture with attention layers and relative position encodings to conditionally generate data entries, allowing for efficient sampling of conditionally dependent data sequences, such as video frames, by leveraging temporal and spatial attention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed-lag autoregressive models are used to generate video frames, then the model structure is simple and easy to implement, but the model imposes unrealistic conditional independence assumptions that fail to capture long-range coherence in videos
Solution Approach 1:
The patent segments the video generation task into multiple independent data generation tasks, where each task corresponds to generating a specific frame or set of frames given different conditioning frames. This allows the model to capture long-range dependencies by treating different temporal positions as separate generation tasks rather than a single sequential generation process.
Solution Approach 2:
The patent introduces a task dimension to the generation process, where each data generation task is associated with a specific set of conditioning frames and target frames. This task-level abstraction allows the model to reason about temporal relationships across the entire video sequence rather than being limited to fixed-lag dependencies.
2Reliability
If recurrent neural networks are used to model long-range coherence, then the model can theoretically capture dependencies across the entire sequence, but the model must be trained over short sequences or with truncated gradients due to computational constraints
Solution Approach 1:
The patent divides the training process into multiple independent data generation tasks, each focusing on a specific subset of frames. This segmentation allows the model to train on longer sequences by treating different temporal segments as separate tasks, avoiding the need to train the entire sequence at once while still capturing long-range dependencies.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing conditional independence graphs that capture temporal dependencies between frames. These pre-computed graphs serve as guidance during the generation process, allowing the model to efficiently generate frames while respecting long-range temporal relationships without requiring extensive real-time computation.
3Manufacturing precision
If photorealistic image generative models are used to generate video frames, then the model can produce high-quality images, but the resource requirements scale with frame count making long video generation infeasible
Solution Approach 1:
The patent segments the video generation process into multiple independent data generation tasks, where each task generates a specific frame or set of frames. This segmentation allows the model to reuse computational resources across tasks by conditioning on previously generated frames, rather than treating each frame as an independent generation task that requires full computational resources.
Solution Approach 2:
The patent introduces conditional independence graphs as intermediary structures that capture temporal dependencies between frames. These graphs act as mediators that guide the generation process by encoding which frames should condition on which other frames, allowing the model to efficiently allocate computational resources by focusing only on the necessary conditional relationships rather than processing all possible frame combinations.
4Adaptability or versatility
If deep generative models based on recurrent neural networks are used, then the model can theoretically impose no conditional independence assumptions, but in practice they must be trained over short sequences or with truncated gradients
Solution Approach 1:
The patent segments the training process into multiple independent data generation tasks, each focusing on a specific temporal subset. This segmentation allows the model to train on longer sequences by distributing the training across multiple tasks rather than requiring a single long training sequence, thereby reducing the time loss associated with training entire long videos.
Solution Approach 2:
The patent performs preliminary actions by pre-computing conditional independence graphs that capture temporal dependencies. These pre-computed graphs are stored and reused during generation, allowing the model to quickly generate frames without performing extensive real-time computation or training, thus reducing the effective training time while maintaining flexibility in modeling long-range dependencies.
Data Source
AI summary
Methods, systems, and techniques for generating one or more conditionally dependent data entries using a probabilistic generative model, and for training that model. The probabilistic generative model is trained using a plurality of data generation tasks respectively corresponding to a plurality of vectors each having a sequence of differently indexed data entries that are conditionally dependent on each other. Each of the data generation tasks involves generating at least one latent data entry selected from the sequence of data entries in response to being provided at least one index for each of the at least one latent data entry. Training can be performed by minimizing an expected value of a denoising loss over all training stages.