Soft-to-Hard Vector Quantization: A Novel End-to-End Learning Method for Compressible Representations

This paper introduces a novel end-to-end learning method for compressing representations based on vector quantization, termed Soft-to-Hard Vector Quantization (SHVQ). This approach combines soft and hard quantization, enabling efficient compression and high-quality reconstruction simultaneously. The core idea of the SHVQ method is to encode input data into a set of soft quantized vectors and gradually transform these vectors into hard quantized vectors during training, achieving the compression goal.

Specifically, SHVQ employs a two-step process for vector quantization. First, input data is encoded into a set of soft quantized vectors, which can be learned through gradient descent. Then, these soft quantized vectors are mapped onto a set of hard quantized vectors using the K-means clustering algorithm. During training, the soft quantized vectors gradually become mapped to hard quantized vectors, achieving more efficient compression.

Experimental results demonstrate that the SHVQ method achieves efficient compression and high-quality reconstruction on multiple datasets, outperforming traditional vector quantization methods. Furthermore, SHVQ exhibits excellent scalability and generalization capabilities, applicable to diverse datasets and tasks.

In summary, the proposed SHVQ method presents a novel perspective and approach for vector quantization and end-to-end learning of compressed representations, with vast potential applications.