Abstract: This paper presents Bottom-up Residual vector quantization for learned Image Compression (BRIC). This novel deep learning-based image compression method quantizes latent representations ...
Huawei’s Zurich Computing Systems Laboratory has released SINQ (Sinkhorn Normalization Quantization), an open-source quantization method that reduces the memory requirements of large language models ...
This project aims to integrate BBQ into the OpenSearch k-NN plugin to offer users a memory-efficient alternative, ideal for large-scale vector workloads in constrained compute environments. The ...
Abstract: In recent years, few-shot detection has become a popular research direction in the field of industrial defect detection, which aims to perform defect detection tasks accurately using a ...
Quantization is an essential technique in machine learning for compressing model data, which enables the efficient operation of large language models (LLMs). As the size and complexity of these models ...
Text-to-image diffusion models have made significant strides in generating complex and faithful images from input conditions. Among these, Diffusion Transformers Models (DiTs) have emerged as ...
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into ...
Product quantization (PQ) is an effective vector quantization method. A product quantizer can generate an exponentially large codebook at very low memory/time cost. The essence of PQ is to decompose ...
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