HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories (2412.17040v2)

Abstract: To efficiently adapt large models or to train generative models of neural representations, Hypernetworks have drawn interest. While hypernetworks work well, training them is cumbersome, and often requires ground truth optimized weights for each sample. However, obtaining each of these weights is a training problem of its own-one needs to train, e.g., adaptation weights or even an entire neural field for hypernetworks to regress to. In this work, we propose a method to train hypernetworks, without the need for any per-sample ground truth. Our key idea is to learn a Hypernetwork Field and estimate the entire trajectory of network weight training instead of simply its converged state. In other words, we introduce an additional input to the Hypernetwork, the convergence state, which then makes it act as a neural field that models the entire convergence pathway of a task network. A critical benefit in doing so is that the gradient of the estimated weights at any convergence state must then match the gradients of the original task -- this constraint alone is sufficient to train the Hypernetwork Field. We demonstrate the effectiveness of our method through the task of personalized image generation and 3D shape reconstruction from images and point clouds, demonstrating competitive results without any per-sample ground truth.

• The paper introduces HyperNet Fields, a method that trains hypernetworks without ground truth by modeling full weight convergence trajectories.
• It employs a gradient matching constraint at each convergence state to align estimated weights with natural training dynamics in tasks like image generation and 3D reconstruction.
• Results demonstrate substantially lower computational costs and comparable performance to traditional methods, highlighting its potential for scalable neural network adaptation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories

Overview and Contribution

The paper introduces a novel approach to hypernetwork training, termed HyperNet Fields, which eschews the need for ground truth weight optimization, instead learning to navigate the entire trajectory of weight convergence. This method leverages the concept of modeling convergence dynamics for efficient training of hypernetworks, thereby offering potentially significant reductions in computational overhead. By focusing on the trajectory of network weights rather than the final optimal state, the authors propose a paradigm shift away from traditional methods that require precomputed, sample-specific weights. This approach not only addresses the typical computational bottlenecks faced by conventional methods but also enhances the flexibility and scalability of hypernetworks when applied to large datasets.

Methodology

HyperNet Fields are formulated by training hypernetworks to estimate the full optimization pathway of a task-specific network's weights rather than just their final converged state. This is achieved by introducing a convergence state as an additional input to the hypernetwork, effectively transforming it into a neural field that models the entire convergence pathway. The central insight is that by ensuring the gradient of the estimated weights aligns with the original task's gradients, effective training of the hypernetwork fields can be achieved without the need for ground-truth weights.

The training process of HyperNet Fields diverges from traditional methods by eliminating the requirement for precomputed converged weights. Instead, the fields are trained through a gradient matching constraint applied at each point along the trajectory. This constraint ensures the hypernetwork's output at any convergence state aligns with the natural trajectory of network training, thus capturing the dynamic changes in weight space across optimization steps.

Results and Evaluation

The paper applies this methodology to two distinct applications: personalized image generation and 3D shape reconstruction. For personalized image generation, leveraging the DreamBooth network as the task-specific model, HyperNet Fields are shown to achieve comparable performance to existing methods like HyperDreamBooth and Textual Inversion, while significantly reducing the computational costs involved. Specifically, the training time on the CelebA-HQ dataset is reduced to a fraction (approximately four times less) compared to HyperDreamBooth. Metrics such as CLIP-I, CLIP-T, and DINO demonstrate the successful image and text alignment of generated images without foregoing visual quality.

In the domain of 3D shape reconstruction, the method showcases its versatility by efficiently estimating weights of occupancy networks from sparse input data like point clouds and rendered images. The ability of HyperNet Fields to generate accurate 3D reconstructions with reduced computational demands highlights its potential for broader applications in fields requiring large-scale model adaptation at reduced computational cost.

Implications

The implications of HyperNet Fields are significant both theoretically and practically. By enabling hypernetwork training without ground truth, this method challenges the necessity of target weight computation in hypernetwork methodologies. This allows for scaling hypernetwork applications to unprecedented dataset sizes without prohibitive computation, making it feasible to apply hypernetwork techniques in real-world, large-scale scenarios.

Theoretically, this approach raises intriguing questions about the dynamics of neural optimization and the role of implicit trajectory modeling in neural network settings. The concept of learning to predict trajectories, rather than endpoint solutions, might inspire further research into neural models capable of capturing and utilizing such dynamism.

Future Directions

Looking ahead, there are several avenues for extending this work. The adaptation of HyperNet Fields to other complex networks, such as NeRFs for scene reconstruction, or the integration with LLMs to personalize outputs based on specific styles or semantics, represents exciting possibilities. Additionally, exploring this methodology in the context of other generative tasks where model adaptation is critical could yield further insights into the versatility and adaptability of hypernetwork fields.

The paper positions HyperNet Fields as a robust, efficient alternative to current hypernetwork training paradigms, underscoring a significant stride towards more adaptable, computationally viable neural networks. This contribution is well-poised to impact ongoing research and application in the field of hypernetworks and beyond.