  In this paper, we propose a novel Denoising Model for Representation Learning
and take Person Re-Identification (ReID) as a benchmark task, named
DenoiseReID, to improve feature discriminative with joint feature extraction
and denoising. In the deep learning epoch, backbones which consists of cascaded
embedding layers (e.g. convolutions or transformers) to progressively extract
useful features, becomes popular. We first view each embedding layer in a
backbone as a denoising layer, processing the cascaded embedding layers as if
we are recursively denoise features step-by-step. This unifies the frameworks
of feature extraction and feature denoising, where the former progressively
embeds features from low-level to high-level, and the latter recursively
denoises features step-by-step. Then we design a novel Feature Extraction and
Feature Denoising Fusion Algorithm (FEFDFA) and \textit{theoretically
demonstrate} its equivalence before and after fusion. FEFDFA merges parameters
of the denoising layers into existing embedding layers, thus making feature
denoising computation-free. This is a label-free algorithm to incrementally
improve feature also complementary to the label if available. Besides, it
enjoys two advantages: 1) it's a computation-free and label-free plugin for
incrementally improving ReID features. 2) it is complementary to the label if
the label is available. Experimental results on various tasks (large-scale
image classification, fine-grained image classification, image retrieval) and
backbones (transformers and convolutions) show the scalability and stability of
our method. Experimental results on 4 ReID datasets and various of backbones
show the stability and impressive improvements. We also extend the proposed
method to large-scale (ImageNet) and fine-grained (e.g. CUB200) classification
tasks, similar improvements are proven.
