Diversified Texture Synthesis with Feed-forward Networks (1703.01664v1)

Abstract: Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization.

Overview of "Diversified Texture Synthesis with Feed-forward Networks"

This paper addresses the challenge of synthesizing diverse and complex textures efficiently using deep generative models. Existing feed-forward texture synthesis techniques have limitations related to generality, diversity, and the quality of synthesized visual results. The authors propose a deep generative feed-forward network designed to synthesize multiple textures concurrently, which also allows the model to interpolate between these textures in a meaningful way.

The paper introduces an architecture composed of a generator and a selector network aimed at producing texture images from a noise vector and a selection unit. The network is tasked with generating diverse samples by leveraging the noise vector, which enables it to create various outputs from a single exemplar. This is distinct from prior methods that could produce only visually identical outputs for each texture.

Key Contributions

1. Generative Model for Multi-texture Synthesis: The authors designed a single network capable of synthesizing multiple textures interchangeably. This network uses a one-hot encoded selection unit to switch between different textures dynamically.
2. Diversity Enhancement Techniques: A diversity loss function is introduced to ensure the network generates varied outputs instead of being confined to a single repetitive pattern for each texture.
3. Incremental Learning Strategy: To address convergence difficulties, the authors propose an incremental training paradigm. This approach involves progressively exposing the network to new textures only after previous textures have been well-learned, thus ensuring better generalization and memory retention.

Numerical Results and Claims

The paper showcases strong experimental results, demonstrating the efficacy of the proposed network. In particular, the network is trained to synthesize up to 300 diverse textures proficiently, with the ability to interpolate between them. Visual comparisons illustrate that the proposed model maintains or surpasses the quality of outputs from networks trained on individual textures separately. Additionally, training with the diversity loss leads to perceptually different images for the same texture, highlighting the model's robustness in producing diverse textures.

Implications and Future Directions

Practically, this model reduces the computational resources required for storing and managing multiple texture-specific models. Theoretically, it contributes to the ongoing exploration of multi-tasking capabilities in neural networks. The approach aligns with efforts in developing models that can learn and generalize over multiple tasks efficiently.

Future work could explore further reductions in network complexity while maximizing the number of styles a single network can handle. Additionally, exploring the integration of this methodology into applications such as neural style transfer, spatial texture generation, and real-time image editing could offer practical enhancements. Continued improvement in learning strategies, especially in managing exceedingly high-dimensional texture spaces and achieving faster convergence without sacrificing diversity, remains an open area for research.

In conclusion, this paper presents a crafted solution for the complexity of texture synthesis using a single generalized model to produce diverse and high-fidelity results. The combination of deep generative modeling with novel training techniques offers a significant advancement in the field of neural texture synthesis.