147 research outputs found
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a
limited amount of training data, encounter several challenges for generating
high-quality samples. Since data augmentation strategies have largely
alleviated the training instability, how to further improve the generative
performance of DE-GANs becomes a hotspot. Recently, contrastive learning has
shown the great potential of increasing the synthesis quality of DE-GANs, yet
related principles are not well explored. In this paper, we revisit and compare
different contrastive learning strategies in DE-GANs, and identify (i) the
current bottleneck of generative performance is the discontinuity of latent
space; (ii) compared to other contrastive learning strategies,
Instance-perturbation works towards latent space continuity, which brings the
major improvement to DE-GANs. Based on these observations, we propose FakeCLR,
which only applies contrastive learning on perturbed fake samples, and devises
three related training techniques: Noise-related Latent Augmentation,
Diversity-aware Queue, and Forgetting Factor of Queue. Our experimental results
manifest the new state of the arts on both few-shot generation and limited-data
generation. On multiple datasets, FakeCLR acquires more than 15% FID
improvement compared to existing DE-GANs. Code is available at
https://github.com/iceli1007/FakeCLR.Comment: Accepted by ECCV202
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Generative Adversarial Networks (GANs) have achieved remarkable achievements
in image synthesis. These successes of GANs rely on large scale datasets,
requiring too much cost. With limited training data, how to stable the training
process of GANs and generate realistic images have attracted more attention.
The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three
aspects: (i) Mismatch Between Training and Target Distributions, (ii)
Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data
Spaces. Although many augmentation and pre-training strategies have been
proposed to alleviate these issues, there lacks a systematic survey to
summarize the properties, challenges, and solutions of DE-GANs. In this paper,
we revisit and define DE-GANs from the perspective of distribution
optimization. We conclude and analyze the challenges of DE-GANs. Meanwhile, we
propose a taxonomy, which classifies the existing methods into three
categories: Data Selection, GANs Optimization, and Knowledge Sharing. Last but
not the least, we attempt to highlight the current problems and the future
directions.Comment: Under revie
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Neural network pruning is an essential technique for reducing the size and
complexity of deep neural networks, enabling large-scale models on devices with
limited resources. However, existing pruning approaches heavily rely on
training data for guiding the pruning strategies, making them ineffective for
federated learning over distributed and confidential datasets. Additionally,
the memory- and computation-intensive pruning process becomes infeasible for
recourse-constrained devices in federated learning. To address these
challenges, we propose FedTiny, a distributed pruning framework for federated
learning that generates specialized tiny models for memory- and
computing-constrained devices. We introduce two key modules in FedTiny to
adaptively search coarse- and finer-pruned specialized models to fit deployment
scenarios with sparse and cheap local computation. First, an adaptive batch
normalization selection module is designed to mitigate biases in pruning caused
by the heterogeneity of local data. Second, a lightweight progressive pruning
module aims to finer prune the models under strict memory and computational
budgets, allowing the pruning policy for each layer to be gradually determined
rather than evaluating the overall model structure. The experimental results
demonstrate the effectiveness of FedTiny, which outperforms state-of-the-art
approaches, particularly when compressing deep models to extremely sparse tiny
models. FedTiny achieves an accuracy improvement of 2.61% while significantly
reducing the computational cost by 95.91% and the memory footprint by 94.01%
compared to state-of-the-art methods.Comment: This paper has been accepted to ICDCS 202
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
In this work, we address the task of few-shot part segmentation, which aims
to segment the different parts of an unseen object using very few labeled
examples. It is found that leveraging the textual space of a powerful
pre-trained image-language model (such as CLIP) can be beneficial in learning
visual features. Therefore, we develop a novel method termed PartSeg for
few-shot part segmentation based on multimodal learning. Specifically, we
design a part-aware prompt learning method to generate part-specific prompts
that enable the CLIP model to better understand the concept of ``part'' and
fully utilize its textual space. Furthermore, since the concept of the same
part under different object categories is general, we establish relationships
between these parts during the prompt learning process. We conduct extensive
experiments on the PartImageNet and PascalPart datasets, and the
experimental results demonstrated that our proposed method achieves
state-of-the-art performance
Site selection of LNG terminal based on cloud matter element model and principal component analysis
With the development of liquefied natural gas(LNG) port, as one of the crucial LNG port sitting process, the LNG terminal site’s condition assessment method has always received attention from experts, scholars concern more and more about the method’s practicality and reliability. In the traditional condition assessment method, due to the characteristics of the complex and extensive factors in the comprehensive assessment of the LNG terminal site, the assessment system is not comprehensive enough, or the assessment is too complex, the indexes are not easy to quantify, such problems are emerging. In view of the above reasons, the principal component analysis(PCA) method is used to transform the multi-indicators that affect the comparison of terminal sites into a few comprehensive indicators. A comprehensive evaluation model of the LNG terminal site based on cloud matter element theory and subjective and objective comprehensive weighting method was constructed. By the subjective and objective comprehensive weighting method, the comprehensive weight of each index is determined and the LNG terminal site comprehensive assessment standard cloud element model is constructed with the combination of cloud model and matter-element theory. The cloud matter-element correlation function is established to determine the degree of association between the matter element to be evaluated and the standard cloud matter element model. In order to eliminate random errors and improve the credibility of the results, the algorithm is used for multiple calculations and analysis to achieve the purpose of simultaneously giving the evaluation results and coefficients of credible degree. Finally, the reliability and rationality of the method are verified by an example
Efficient photocatalytic degradation of Malachite Green in seawater by the hybrid of Zinc-Oxide Nanorods Grown on Three-Dimensional (3D) reduced graphene oxide(RGO)/Ni foam
A hybrid of ZnO nanorods grown onto three-dimensional (3D) reduced graphene oxide (RGO)@Ni foam (ZnO/RGO@NF) is synthesized by a facile hydrothermal method. The as-prepared hybrid material is physically characterized by SEM, XRD, Raman, and X-ray photoelectron spectroscopy (XPS).When the as-prepared 3D hybrid is investigated as a photocatalyst, it demonstrates significant high photocatalytic activity for the degradation of methylene blue (MB), rhodamine (RhB), and mixed MB/RhB as organic dye pollutants. In addition, the practical application and the durability of the as-prepared catalyst to degradation of malachite green (MG) in seawater are firstly assessed in a continuous flow system. The catalyst shows a high degradation efficiency and stable photocatalytic activity for 5 h continuous operation, which should be a promising catalyst for the degradation of organic dyes in seawater
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