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.

我们提出了一种用于表示学习的新型去噪模型，并以人员重识别（ReID）作为基准任务，称为 DenoiseReID，通过联合特征提取和去噪来改进特征的区分能力。

DenoiseReID: 人物再识别表示学习的去噪模型

DenoiseReID: Denoising Model for Representation Learning of Person  Re-Identification

Diffusion models have demonstrated remarkable efficacy in various generative
tasks with the predictive prowess of denoising model. Currently, these models
employ a uniform denoising approach across all timesteps. However, the inherent
variations in noisy latents at each timestep lead to conflicts during training,
constraining the potential of diffusion models. To address this challenge, we
propose a novel two-stage training strategy termed Step-Adaptive Training. In
the initial stage, a base denoising model is trained to encompass all
timesteps. Subsequently, we partition the timesteps into distinct groups,
fine-tuning the model within each group to achieve specialized denoising
capabilities. Recognizing that the difficulties of predicting noise at
different timesteps vary, we introduce a diverse model size requirement. We
dynamically adjust the model size for each timestep by estimating task
difficulty based on its signal-to-noise ratio before fine-tuning. This
adjustment is facilitated by a proxy-based structural importance assessment
mechanism, enabling precise and efficient pruning of the base denoising model.
Our experiments validate the effectiveness of the proposed training strategy,
demonstrating an improvement in the FID score on CIFAR10 by over 0.3 while
utilizing only 80\% of the computational resources. This innovative approach
not only enhances model performance but also significantly reduces
computational costs, opening new avenues for the development and application of
diffusion models.

提出了一种名为 Step-Adaptive Training 的创新训练策略，通过在初始阶段训练一个基础去噪模型来涵盖所有时间步长，然后将时间步长分成不同的组，在每个组内进行微调以实现专门的去噪能力，这种方法不仅提高了模型性能，还显著降低了计算成本。

并非所有步骤相等：高效生成与渐进传播模型

Not All Steps are Equal: Efficient Generation with Progressive Diffusion  Models

Post-editing in Automatic Speech Recognition (ASR) entails automatically
correcting common and systematic errors produced by the ASR system. The outputs
of an ASR system are largely prone to phonetic and spelling errors. In this
paper, we propose to use a powerful pre-trained sequence-to-sequence model,
BART, further adaptively trained to serve as a denoising model, to correct
errors of such types. The adaptive training is performed on an augmented
dataset obtained by synthetically inducing errors as well as by incorporating
actual errors from an existing ASR system. We also propose a simple approach to
rescore the outputs using word level alignments. Experimental results on
accented speech data demonstrate that our strategy effectively rectifies a
significant number of ASR errors and produces improved WER results when
compared against a competitive baseline. We also highlight a negative result
obtained on the related grammatical error correction task in Hindi language
showing the limitation in capturing wider context by our proposed model.

该研究使用预训练序列到序列模型 BART，并对其进行自适应训练来对自动语音识别中的发音和拼写错误进行纠正，并采用词级对齐的简单方法重新评分。实验结果表明，该策略在口音语音数据上有效地矫正了大量 ASR 错误，并在与基准系统的比较中产生了改进的 WER 结果。然而，在印地语语法错误纠正任务中，该模型捕捉更广泛上下文的能力受到限制。