Unsupervised Semantic Hashing with Pairwise Reconstruction
This work addresses the need for better similarity search in large datasets, but it is incremental as it builds on existing weak supervision methods.
The paper tackles the problem of efficient similarity search in large-scale datasets by proposing PairRec, an unsupervised semantic hashing method that uses pairwise reconstruction to encode local neighborhood structures, resulting in significant performance improvements over traditional and state-of-the-art approaches.
Semantic Hashing is a popular family of methods for efficient similarity search in large-scale datasets. In Semantic Hashing, documents are encoded as short binary vectors (i.e., hash codes), such that semantic similarity can be efficiently computed using the Hamming distance. Recent state-of-the-art approaches have utilized weak supervision to train better performing hashing models. Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model. PairRec first encodes weakly supervised training pairs (a query document and a semantically similar document) into two hash codes, and then learns to reconstruct the same query document from both of these hash codes (i.e., pairwise reconstruction). This pairwise reconstruction enables our model to encode local neighbourhood structures within the hash code directly through the decoder. We experimentally compare PairRec to traditional and state-of-the-art approaches, and obtain significant performance improvements in the task of document similarity search.