Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper therefore proposes the Gaussian clouded logit adjustment by Gaussian perturbing different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in the classifier, we accordingly propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method.

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.