On Pre-Trained Image Features and Synthetic Images for Deep Learning (1710.10710v2)

Abstract: Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling. Using synthetic images is therefore very attractive to train object detectors, as the labeling comes for free, and several approaches have been proposed to combine synthetic and real images for training. In this paper, we show that a simple trick is sufficient to train very effectively modern object detectors with synthetic images only: We freeze the layers responsible for feature extraction to generic layers pre-trained on real images, and train only the remaining layers with plain OpenGL rendering. Our experiments with very recent deep architectures for object recognition (Faster-RCNN, R-FCN, Mask-RCNN) and image feature extractors (InceptionResnet and Resnet) show this simple approach performs surprisingly well.

Summary of "On Pre-Trained Image Features and Synthetic Images for Deep Learning"

This paper investigates the use of synthetic images to train deep learning-based object detectors, specifically evaluating the effectiveness of pre-trained feature extractors. The researchers propose a straightforward yet highly efficacious approach to leverage synthetic images without compromising detection accuracy on real-world images—by keeping ('freezing') the weights of feature extractors pre-trained on real images.

Key Contributions

The principal contribution lies in demonstrating that state-of-the-art object detectors can be effectively trained using synthetic images alone, by freezing the weights of a pre-trained feature extractor. This paper challenges the traditional dependence on large amounts of labeled real-world data and sophisticated photo-realistic rendering.

1. Freezing Feature Extractor Layers: Previous research has shown domain gaps between synthetic and real images, typically necessitating complex strategies for domain adaptation. This paper presents a contrarian view, suggesting that the feature extractors trained on real data are robust enough to be applied directly to synthetic images. By freezing these extractors and only training the classification and localization components, the results almost parallel those achieved with fully real datasets.
2. Experiments and Performance Analysis: The paper evaluates various architectures like Faster-RCNN, R-FCN, and Mask-RCNN with feature extractors such as InceptionResnet and Resnet101. Empirical results indicate that the proposed approach achieves up to 95% of the performance relative to real-image-only training. Furthermore, this method significantly surpasses traditional strategies, where feature extractors are retrained on synthetic data.
3. Synthetic Image Generation Pipeline: The paper details a pipeline where objects are rendered using CAD models on varied background images, incorporating techniques like OpenGL rendering, and deals with illumination variability and noise to ensure patch-level realism.
4. Camera Variability: The analysis also involves the impact of different camera image statistics. The observed performance varied across different camera setups, indicating certain cameras achieved better gains possibly due to inherently closer image statistics between their real and synthetic data.
5. Ablation Studies: Detailed ablation experiments highlight that certain pipeline components, such as blurring, significantly enhance the realism of synthetic images, boosting detector performance.

Implications and Future Directions

Practically, the implications of this paper are profound for applications where labeled data acquisition is either cost-prohibitive or infeasible. Automated labeling using synthetic data could streamline tasks in industries ranging from logistics to robotics, where environmental variability is significant.

Theoretically, this research posits that the general background knowledge encoded in pre-trained feature extractors is sufficiently rich, challenging existing paradigms which emphasize the need for domain-specific retraining. This could pave the way for more generalized AI models capable of transferring knowledge across domains with minimal adaptation.

Future work could expand on analyzing the limits of patch-level realism affecting detector performance and explore adaptive techniques that progressively unfreeze layers based on performance feedback loops. Further studies could also delve into using this methodology for other vision tasks, such as segmentation and tracking, potentially involving adversarial methods to close any residual domain gaps.

The proposed approach represents a significant step in making synthetic training methodologies more viable, economically feasible, and implementable at scale for real-world applications.