LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes (2106.01487v2)

Abstract: Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional neural representations to low-dimensional binary codes is a challenging task and often require large bit-codes to be accurate. In this work, we propose a novel method for Learning Low-dimensional binary Codes (LLC) for instances as well as classes. Our method does not require any side-information, like annotated attributes or label meta-data, and learns extremely low-dimensional binary codes (~20 bits for ImageNet-1K). The learnt codes are super-efficient while still ensuring nearly optimal classification accuracy for ResNet50 on ImageNet-1K. We demonstrate that the learnt codes capture intrinsically important features in the data, by discovering an intuitive taxonomy over classes. We further quantitatively measure the quality of our codes by applying it to the efficient image retrieval as well as out-of-distribution (OOD) detection problems. For ImageNet-100 retrieval problem, our learnt binary codes outperform 16 bit HashNet using only 10 bits and also are as accurate as 10 dimensional real representations. Finally, our learnt binary codes can perform OOD detection, out-of-the-box, as accurately as a baseline that needs ~3000 samples to tune its threshold, while we require none. Code is open-sourced at https://github.com/RAIVNLab/LLC.

• The paper introduces a novel two-phase framework that learns class codes via surrogate classification and instance codes using the ECOC framework without auxiliary data.
• The method achieves 74.5% classification accuracy on ImageNet-1K with only 20-bit codes, nearly matching full-dimensional feature performance.
• The study also demonstrates that the learned binary codes outperform existing hashing methods in retrieval tasks and facilitate out-of-distribution detection without additional tuning.

Overview of "Accurate, Multi-purpose Learnt Low-dimensional Binary Codes"

In the paper titled "Accurate, Multi-purpose Learnt Low-dimensional Binary Codes," the authors tackle the challenge of learning efficient binary codes for both instances and classes in a large-scale setting. This work focuses on embedding data into low-dimensional binary spaces, a critical task within machine learning, particularly relevant to computer vision and efficient data retrieval systems. The proposed method demonstrates how low-dimensional codes—approximately 20 bits for datasets like ImageNet-1K—can be learned without auxiliary information, such as annotated attributes, while maintaining classification accuracy nearly comparable to standard methods using real-valued representations.

Methodology

The authors introduce a novel two-phase approach to learn binary codes, termed in the paper but not specifically named in this summary. Initially, in Phase 1, binary codes for classes are learned through a surrogate classification task using a multi-class dataset. By leveraging a neural network's deep features, the method produces low-dimensional binary codes for classes without the need for side-information or predefined taxonomies. In Phase 2, these class codes are then employed to learn binary codes for instances using the Error-Correcting Output Codes (ECOC) framework, thereby utilizing the semantic structure learned in the first phase. This approach allows the computation cost to grow sub-linearly with the number of classes, demonstrating efficiency in both training and inference.

Results

For ImageNet-1K, the proposed method achieves binary codes that facilitate classification accuracy of 74.5% with only 20-bit codes, compared to 77% when using the full-dimensional ResNet50 features, a marginal trade-off considering the space and potential efficiency gains. Further experiments for image retrieval, particularly on ImageNet-100, reveal that these binary codes outperform existing methods like HashNet despite using significantly fewer bits, such as achieving higher accuracy with 16-bit and 32-bit codes.

Additionally, a notable application of these learned codes is in out-of-distribution (OOD) detection where the codes can indicate whether an instance is within the distribution or not, without needing tuning parameters from samples—demonstrating a potent combination of accuracy and practicality.

Implications and Future Work

The implications of this work are multifaceted. Practically, it shows promise for deploying efficient systems in scenarios with massive datasets or resource constraints. Theoretically, it raises intriguing questions about the limits of representational efficiency and the nature of minimalistic representations. The feature separability in learned binary space implies not only potential improvements in efficient classification but also opportunities in developing interpretable models and semantic embeddings without requiring vast amounts of labeled data.

Future research could explore extensions in multi-modal data settings, leveraging binary codes in deep cross-modal retrieval, or investigating hierarchical learning mechanisms for naturally structured outputs. The speculative direction of integrating weak supervision or incorporating human-centered priors could potentially address the interpretability limitations observed in binary semantic splits.

Overall, this paper contributes a robust approach to efficient representation learning with compelling advantages in classification and retrieval, laying a foundation for further in-depth exploration and application in scaling up AI models for practical deployment.