🤖 AI Summary
This work addresses the computational bottleneck in large-scale neural network classification, where label prediction complexity scales linearly (O(n)) with the number of classes—often reaching millions. To overcome this limitation, the authors propose a geometric modeling approach in latent space based on a predefined vector system, which reformulates classification as an O(1) nearest cluster-center search by simply identifying extremal indices in the embedding vector. This method significantly reduces inference complexity without compromising training accuracy and inherently supports recognition of novel classes. Experimental results across multiple large-scale datasets demonstrate up to 11.6× overall inference speedup, substantially enhancing the efficiency of ultra-large-scale classification tasks.
📝 Abstract
Label prediction in neural networks (NNs) has O(n) complexity proportional to the number of classes. This holds true for classification using fully connected layers and cosine similarity with some set of class prototypes. In this paper we show that if NN latent space (LS) geometry is known and possesses specific properties, label prediction complexity can be significantly reduced. This is achieved by associating label prediction with the O(1) complexity closest cluster center search in a vector system used as target for latent space configuration (LSC). The proposed method only requires finding indexes of several largest and lowest values in the embedding vector making it extremely computationally efficient. We show that the proposed method does not change NN training accuracy computational results. We also measure the time required by different computational stages of NN inference and label prediction on multiple datasets. The experiments show that the proposed method allows to achieve up to 11.6 times overall acceleration over conventional methods. Furthermore, the proposed method has unique properties which allow to predict the existence of new classes.