CLASS NORMALIZATION FOR (CONTINUAL)? GENERALIZED ZERO-SHOT LEARNING

Abstract

Normalization techniques have proved to be a crucial ingredient of successful training in a traditional supervised learning regime. However, in the zero-shot learning (ZSL) world, these ideas have received only marginal attention. This work studies normalization in ZSL scenario from both theoretical and practical perspectives. First, we give a theoretical explanation to two popular tricks used in zero-shot learning: normalize+scale and attributes normalization and show that they help training by preserving variance during a forward pass. Next, we demonstrate that they are insufficient to normalize a deep ZSL model and propose Class Normalization (CN): a normalization scheme, which alleviates this issue both provably and in practice. Third, we show that ZSL models typically have more irregular loss surface compared to traditional classifiers and that the proposed method partially remedies this problem. Then, we test our approach on 4 standard ZSL datasets and outperform sophisticated modern SotA with a simple MLP optimized without any bells and whistles and having ≈50 times faster training speed. Finally, we generalize ZSL to a broader problem -continual ZSL, and introduce some principled metrics and rigorous baselines for this new setup. The source code is available at https://github.com/universome/class-norm.

1. INTRODUCTION

Zero-shot learning (ZSL) aims to understand new concepts based on their semantic descriptions instead of numerous input-output learning pairs. It is a key element of human intelligence and our best machines still struggle to master it (Ferrari & Zisserman, 2008; Lampert et al., 2009; Xian et al., 2018a) . Normalization techniques like batch/layer/group normalization (Ioffe & Szegedy, 2015; Ba et al., 2016; Wu & He, 2018) are now a common and important practice of modern deep learning. But despite their popularity in traditional supervised training, not much is explored in the realm of zero-shot learning, which motivated us to study and investigate normalization in ZSL models. We start by analyzing two ubiquitous tricks employed by ZSL and representation learning practitioners: normalize+scale (NS) and attributes normalization (AN) (Bell et al., 2016; Zhang et al., 2019; Guo et al., 2020; Chaudhry et al., 2019) . Their dramatic influence on performance can be observed from Table 1 . When these two tricks are employed, a vanilla MLP model, described in Sec 3.1, can outperform some recent sophisticated ZSL methods. Normalize+scale (NS) changes logits computation from usual dot-product to scaled cosine similarity: ŷc = z p c =⇒ ŷc = γ • z z 2 γ • p c p c ( ) where z is an image feature, p c is c-th class prototype and γ is a hyperparameter, usually picked from [5, 10] interval (Li et al., 2019; Zhang et al., 2019) . Scaling by γ is equivalent to setting a high temperature of γ 2 in softmax. In Sec. 3.2, we theoretically justify the need for this trick and explain why the value of γ must be so high.

