Deep Feature Interpolation for Image Content Changes (1611.05507v2)

Abstract: We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, it relies only on simple linear interpolation of deep convolutional features from pre-trained convnets. We show that despite its simplicity, DFI can perform high-level semantic transformations like "make older/younger", "make bespectacled", "add smile", among others, surprisingly well - sometimes even matching or outperforming the state-of-the-art. This is particularly unexpected as DFI requires no specialized network architecture or even any deep network to be trained for these tasks. DFI therefore can be used as a new baseline to evaluate more complex algorithms and provides a practical answer to the question of which image transformation tasks are still challenging in the rise of deep learning.

• The paper presents a method that uses linear interpolation in CNN feature spaces to achieve controllable image content transformations.
• It leverages pre-trained models like VGG-19 to compute attribute vectors that guide modifications such as aging or expression changes in faces.
• Empirical results demonstrate DFI’s effectiveness in preserving image coherence and outperforming traditional generative models in high-resolution tasks.

Deep Feature Interpolation for Image Content Changes

The paper "Deep Feature Interpolation for Image Content Changes" presents an innovative approach to image transformation based on linear interpolation in pre-trained convolutional neural network (CNN) feature spaces. The method, termed Deep Feature Interpolation (DFI), capitalizes on the capability of CNNs to transform non-linear pixel space data into a more linear, Euclidean-like feature space. This enables effective image content manipulation through simple linear operations without the need for specifically trained deep networks dedicated to each transformation task.

Methodology and Results

DFI operates by utilizing the deep feature representations obtained from CNNs, such as the VGG model trained on the ImageNet dataset. The process is guided by the assumption that CNNs transform image data into a space where class representations are approximately linearly separable. Thus, specific high-level transformations, such as "making a face appear older" or "adding a smile," can be executed by moving along a direction vector in this feature space derived from the mean difference between source and target attribute sets.

Notable technical aspects of DFI include:

• Feature Representation: Input images are mapped to their deep feature representations using layers from pre-trained networks like VGG-19, focusing on convolutional layers that balance linearization with detail retention.
• Attribute Vector Calculation: For a given transformation task, an attribute vector is calculated as the difference between the mean feature vectors of target and source images, both selected based on vector similarity or shared attributes.
• Image Reconstruction: The altered image is reconstructed in pixel space using optimization-based inversion of the modified feature representation, ensuring that the result maintains visual coherence and realism.

Empirical evaluations demonstrate DFI’s capability across several domains, notably facial attribute modification and inpainting tasks. The method effectively handles high-resolution images, outperforming several generative models, like adversarial autoencoders, in terms of identity preservation and transformation quality in many cases. Moreover, the approach's utility as a new baseline is highlighted by its simplicity and competitive results, obviating the necessity for specialized and intricate model architectures.

Implications and Future Prospects

DFI’s remarkable performance without specialized network architectures exposes potential oversimplifications in current evaluation benchmarks used for generative models. Tasks traditionally deemed complex, such as face attribute alteration, are shown to be attainable with linear interpolation in accurately chosen feature spaces. Therefore, this suggests a re-evaluation of benchmarks and encourages developing more challenging and comprehensive datasets for generative models.

Additionally, the findings underscore the usability of pre-trained discriminative networks for tasks beyond mere classification, further broadening their applicability to content generation and transformation realms. Given its tractability and capability to handle high-resolution imagery, DFI could inspire integration into real-time applications, thus paving the way for future optimizations aimed at increasing inference speed and reducing computation costs.

In conclusion, Deep Feature Interpolation exemplifies how well-designed linear operations within the context of rich feature spaces can bridge the gap between discriminative learning and creative image transformation, potentially influencing both practical applications and theoretical advancements in computer vision.