Binary Generative Adversarial Networks for Image Retrieval

Abstract: The most striking successes in image retrieval using deep hashing have mostly involved discriminative models, which require labels. In this paper, we use binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way. By restricting the input noise variable of generative adversarial networks (GAN) to be binary and conditioned on the features of each input image, BGAN can simultaneously learn a binary representation per image, and generate an image plausibly similar to the original one. In the proposed framework, we address two main problems: 1) how to directly generate binary codes without relaxation? 2) how to equip the binary representation with the ability of accurate image retrieval? We resolve these problems by proposing new sign-activation strategy and a loss function steering the learning process, which consists of new models for adversarial loss, a content loss, and a neighborhood structure loss. Experimental results on standard datasets (CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly outperforms existing hashing methods by up to 107\% in terms of~mAP (See Table tab.res.map.comp) Our anonymous code is available at: https://github.com/htconquer/BGAN.

• The paper introduces Binary Generative Adversarial Networks (BGAN) to generate binary codes directly from images without supervision, using a novel sign-activation strategy and tailored loss functions.
• Experimental results show BGAN outperforms existing hashing techniques on standard datasets like CIFAR-10, NUSWIDE, and Flickr, achieving mAP improvements up to 107%.
• This unsupervised approach has significant implications for efficient image retrieval systems and embedding tasks, particularly in settings with limited labeled data.

Binary Generative Adversarial Networks for Image Retrieval

The paper "Binary Generative Adversarial Networks for Image Retrieval" authored by Jingkuan Song introduces a novel approach utilizing Binary Generative Adversarial Networks (BGAN) to address challenges in image retrieval tasks. The focus is on embedding images into binary codes without supervision and enhancing retrieval accuracy using generative models. This approach diverges from conventional deep hashing techniques, which typically rely heavily on labeled data for training discriminative models.

Key Contributions

The paper makes significant contributions to the field of image retrieval through the introduction of BGAN, which aims to generate binary codes directly from images and utilize these codes for effective retrieval. The key contributions include:

1. Direct Binary Code Generation: BGAN introduces a method to embed images into binary codes directly without relaxing constraints. This is achieved through a novel sign-activation strategy and a tailored loss function consisting of adversarial loss, content loss, and neighborhood structure loss.
2. Enhanced Image Retrieval: By conditioning the input noise variables as binary and linking them to the features of input images, BGAN generates images that resemble originals while simultaneously learning binary representations. This method explicitly addresses the typical disconnect between generated representations and retrieval efficacy.
3. Unsupervised Approach: Unlike traditional methods dependent on labeled datasets, BGAN operates independently from any form of supervisory labels. It utilizes a GAN framework to refine features and learn binary codes efficiently, drawing on adversarial training to improve generated image realism.

Experimental Results

The experimental evaluation performed on standard datasets such as CIFAR-10, NUSWIDE, and Flickr demonstrates BGAN's superiority over existing hashing techniques, with improvements in mAP scores up to 107%. This improvement underscores the validity of using generative models in unsupervised hashing and the potential of BGAN architectures for embedding tasks.

Technical Insights

BGAN's architecture comprises:

• Encoder Component: A convolutional network similar to VGG19 is used for feature extraction from images.
• Hash Layer: Converts continuous image feature representations to binary codes using sign-activation functions designed to eliminate non-smooth sign issues.
• Generator and Discriminator: Mimic the classical GAN setup where the generator crafts binary-based images, while the discriminator evaluates the synthetic output's authenticity against real samples.

Implications and Future Directions

The implications of this work are profound for AI development, particularly in unsupervised learning and retrieval systems capable of functioning with sparse data annotations. Future research could explore:

• Expansion of BGAN malleability in varied contexts beyond image domains, potentially adapting mechanisms for video and other multimedia retrieval.
• Fine-tuning architectural parameters to balance between network complexity and computational efficiency, especially when scaling to large datasets.
• Integration with hybrid supervised/unsupervised training regimes where labels, if available, improve the reliability and precision of binary code embeddings.

Overall, BGAN opens avenues for reconsidering how generative models may redefine image retrieval paradigms in the absence of explicit supervision, pushing boundaries in efficient and effective data compression and retrieval.