To avoid the mode collapse problem in large model video generation, several strategies can be employed. Mode collapse occurs when the generative model produces limited varieties of outputs, focusing on a few modes of the data distribution while ignoring others. Here’s how to mitigate it:
Diverse Training Data: Ensure the training dataset is highly diverse, covering a wide range of scenes, motions, styles, and subjects. This encourages the model to learn a broader set of patterns.
Example: If generating human action videos, include datasets with various actions (e.g., running, dancing, jumping), lighting conditions, camera angles, and backgrounds.
Regularization Techniques: Apply regularization methods such as dropout, weight decay, or gradient penalties to prevent the model from overfitting to certain patterns.
Example: Use spectral normalization or gradient penalty in the discriminator (if using GAN-based architectures) to stabilize training and encourage diversity.
Latent Space Manipulation: Introduce randomness or control mechanisms in the latent space during inference to explore different regions of the learned distribution.
Example: Sample from a wider range of latent vectors or apply latent space interpolation to generate varied outputs.
Reconstruction Loss with Diversity Metrics: Combine reconstruction loss with metrics that explicitly encourage diversity, such as mutual information or diversity loss terms.
Example: Modify the loss function to penalize outputs that are too similar across different samples, ensuring the model generates distinct videos.
Multi-Modal Training Objectives: Use multi-task learning or auxiliary objectives that guide the model to focus on different aspects of the video (e.g., motion, appearance, or audio synchronization).
Example: Incorporate a motion consistency loss alongside the visual loss to ensure varied yet coherent motion patterns.
Sampling Strategies: During inference, use sampling techniques like top-k sampling, nucleus sampling (top-p), or diverse beam search to encourage the generation of varied sequences.
Example: Instead of always selecting the most probable next frame, sample from a broader set of likely options to introduce variability.
Model Architecture Improvements: Design architectures that inherently support diversity, such as hierarchical models or those with explicit disentangled representations for content and motion.
Example: Use a two-stream network where one stream handles appearance and the other handles motion, allowing independent control and variation.
Curriculum Learning: Gradually increase the complexity of the training data or tasks to help the model learn diverse patterns from simple to complex scenarios.
Example: Start with static scenes and gradually introduce dynamic elements like moving objects or changing lighting.
For video generation tasks, leveraging scalable and efficient infrastructure is crucial. Tencent Cloud TI Platform offers tools and services for training large-scale models, including distributed training capabilities and optimized GPU instances. Additionally, Tencent Cloud VOD (Video on Demand) and Media Processing Services can assist in post-processing and delivering generated video content efficiently. These services support high-performance computing needs, enabling smoother experimentation and deployment of video generation models.