115 research outputs found
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization
Domain Generalization (DG) is a fundamental challenge for machine learning
models, which aims to improve model generalization on various domains. Previous
methods focus on generating domain invariant features from various source
domains. However, we argue that the domain variantions also contain useful
information, ie, classification-aware information, for downstream tasks, which
has been largely ignored. Different from learning domain invariant features
from source domains, we decouple the input images into Domain Expert Features
and noise. The proposed domain expert features lie in a learned latent space
where the images in each domain can be classified independently, enabling the
implicit use of classification-aware domain variations. Based on the analysis,
we proposed a novel paradigm called Domain Disentanglement Network (DDN) to
disentangle the domain expert features from the source domain images and
aggregate the source domain expert features for representing the target test
domain. We also propound a new contrastive learning method to guide the domain
expert features to form a more balanced and separable feature space.
Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet,
and TerraIncognita demonstrate the competitive performance of our method
compared to the recently proposed alternatives
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Semi-Supervised image classification is one of the most fundamental problem
in computer vision, which significantly reduces the need for human labor. In
this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2,
which formulates various consistency regularizations between labeled and
unlabeled data from the graph perspective. In SimMatchV2, we regard the
augmented view of a sample as a node, which consists of a label and its
corresponding representation. Different nodes are connected with the edges,
which are measured by the similarity of the node representations. Inspired by
the message passing and node classification in graph theory, we propose four
types of consistencies, namely 1) node-node consistency, 2) node-edge
consistency, 3) edge-edge consistency, and 4) edge-node consistency. We also
uncover that a simple feature normalization can reduce the gaps of the feature
norm between different augmented views, significantly improving the performance
of SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervised
learning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs of
training, SimMatchV2 achieves 71.9\% and 76.2\% Top-1 Accuracy with 1\% and
10\% labeled examples on ImageNet, which significantly outperforms the previous
methods and achieves state-of-the-art performance. Code and pre-trained models
are available at
\href{https://github.com/mingkai-zheng/SimMatchV2}{https://github.com/mingkai-zheng/SimMatchV2}
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
This work considers the category distribution heterogeneity in federated
learning. This issue is due to biased labeling preferences at multiple clients
and is a typical setting of data heterogeneity. To alleviate this issue, most
previous works consider either regularizing local models or fine-tuning the
global model, while they ignore the adjustment of aggregation weights and
simply assign weights based on the dataset size. However, based on our
empirical observations and theoretical analysis, we find that the dataset size
is not optimal and the discrepancy between local and global category
distributions could be a beneficial and complementary indicator for determining
aggregation weights. We thus propose a novel aggregation method, Federated
Learning with Discrepancy-aware Collaboration (FedDisco), whose aggregation
weights not only involve both the dataset size and the discrepancy value, but
also contribute to a tighter theoretical upper bound of the optimization error.
FedDisco also promotes privacy-preservation, communication and computation
efficiency, as well as modularity. Extensive experiments show that our FedDisco
outperforms several state-of-the-art methods and can be easily incorporated
with many existing methods to further enhance the performance. Our code will be
available at https://github.com/MediaBrain-SJTU/FedDisco.Comment: Accepted by International Conference on Machine Learning (ICML2023
Can GPT-4 Perform Neural Architecture Search?
We investigate the potential of GPT-4~\cite{gpt4} to perform Neural
Architecture Search (NAS) -- the task of designing effective neural
architectures. Our proposed approach, \textbf{G}PT-4 \textbf{E}nhanced
\textbf{N}eural arch\textbf{I}tect\textbf{U}re \textbf{S}earch (GENIUS),
leverages the generative capabilities of GPT-4 as a black-box optimiser to
quickly navigate the architecture search space, pinpoint promising candidates,
and iteratively refine these candidates to improve performance. We assess
GENIUS across several benchmarks, comparing it with existing state-of-the-art
NAS techniques to illustrate its effectiveness. Rather than targeting
state-of-the-art performance, our objective is to highlight GPT-4's potential
to assist research on a challenging technical problem through a simple
prompting scheme that requires relatively limited domain
expertise\footnote{Code available at
\href{https://github.com/mingkai-zheng/GENIUS}{https://github.com/mingkai-zheng/GENIUS}.}.
More broadly, we believe our preliminary results point to future research that
harnesses general purpose language models for diverse optimisation tasks. We
also highlight important limitations to our study, and note implications for AI
safety
Relational Self-Supervised Learning
Self-supervised Learning (SSL) including the mainstream contrastive learning
has achieved great success in learning visual representations without data
annotations. However, most methods mainly focus on the instance level
information (\ie, the different augmented images of the same instance should
have the same feature or cluster into the same class), but there is a lack of
attention on the relationships between different instances. In this paper, we
introduce a novel SSL paradigm, which we term as relational self-supervised
learning (ReSSL) framework that learns representations by modeling the
relationship between different instances. Specifically, our proposed method
employs sharpened distribution of pairwise similarities among different
instances as \textit{relation} metric, which is thus utilized to match the
feature embeddings of different augmentations. To boost the performance, we
argue that weak augmentations matter to represent a more reliable relation, and
leverage momentum strategy for practical efficiency. The designed asymmetric
predictor head and an InfoNCE warm-up strategy enhance the robustness to
hyper-parameters and benefit the resulting performance. Experimental results
show that our proposed ReSSL substantially outperforms the state-of-the-art
methods across different network architectures, including various lightweight
networks (\eg, EfficientNet and MobileNet).Comment: Extended version of NeurIPS 2021 paper. arXiv admin note: substantial
text overlap with arXiv:2107.0928
CoNe: Contrast Your Neighbours for Supervised Image Classification
Image classification is a longstanding problem in computer vision and machine
learning research. Most recent works (e.g. SupCon , Triplet, and max-margin)
mainly focus on grouping the intra-class samples aggressively and compactly,
with the assumption that all intra-class samples should be pulled tightly
towards their class centers. However, such an objective will be very hard to
achieve since it ignores the intra-class variance in the dataset. (i.e.
different instances from the same class can have significant differences).
Thus, such a monotonous objective is not sufficient. To provide a more
informative objective, we introduce Contrast Your Neighbours (CoNe) - a simple
yet practical learning framework for supervised image classification.
Specifically, in CoNe, each sample is not only supervised by its class center
but also directly employs the features of its similar neighbors as anchors to
generate more adaptive and refined targets. Moreover, to further boost the
performance, we propose ``distributional consistency" as a more informative
regularization to enable similar instances to have a similar probability
distribution. Extensive experimental results demonstrate that CoNe achieves
state-of-the-art performance across different benchmark datasets, network
architectures, and settings. Notably, even without a complicated training
recipe, our CoNe achieves 80.8\% Top-1 accuracy on ImageNet with ResNet-50,
which surpasses the recent Timm training recipe (80.4\%). Code and pre-trained
models are available at
\href{https://github.com/mingkai-zheng/CoNe}{https://github.com/mingkai-zheng/CoNe}
Robust visual tracking via nonlocal regularized multi-view sparse representation
The multi-view sparse representation based visual tracking has attracted increasing attention because the sparse representations of different object features can complement with each other. Since the robustness of different object features is actually not the same in challenging video sequences, it may contain unreliable features (the features with low robustness) in multi-view sparse representation. In this case, how to highlight the useful information of unreliable features for proper multi-feature fusion has become a tough work. To solve this problem, we propose a multi-view discriminant sparse representation method for robust visual tracking, in which we firstly divide the multi-view observations into different groups, and then estimate the sparse representations of multi-view group projections for calculating the observation likelihood. The advantages of the proposed sparse representation method are two-folds: 1) It can properly fuse the observation groups with reliable and unreliable features by using an online updated discriminant matrix to explore the group similarity in multi-feature space. 2) It introduces a nonlocal regularizer to enforce the spatial smoothness among the sparse representations of different group projections, which can enhance the robustness of multi-view sparse representation. Experimental results show that our method can achieve a better tracking performance than state-of-the-art tracking methods d
Impact of hepatic steatosis on treatment response of autoimmune hepatitis: A retrospective multicentre analysis
BackgroundThere is a paucity of data on whether steatosis impacts autoimmune hepatitis (AIH) treatment response. We aimed to evaluate the influence of baseline steatosis on the biochemical response, fibrosis progression, and adverse longterm outcomes of AIH.MethodsSteatosis was diagnosed by a controlled attenuation parameter (CAP) ≥ 248 dB / m. Only patients who underwent immunosuppressive therapy with available liver histological material at diagnosis and qualified CAP within seven days of the liver biopsy were included. Univariate and multivariate analyses were subsequently conducted.ResultsThe multicentre and retrospective cohort enrolled 222 subjects (88.3% female, median age 54 years, median follow-up 48 months) in the final analysis, and 56 (25.2%) patients had hepatic steatosis. Diabetes, hypertension, and significant fibrosis at baseline were more common in the steatosis group than in the no steatosis group. After adjusting for confounding factors, hepatic steatosis was an independent predictor of insufficient biochemical response (OR: 8.07) and identified as an independent predictor of long-term adverse outcomes (HR: 4.07). By subgroup multivariate analysis (different degrees of steatosis, fibrosis, and prednisone dose), hepatic steatosis independently showed a relatively stable correlation with treatment response. Furthermore, in contrast to those without steatosis, a significant increase in liver stiffness (LS) was observed in patients with steatosis (4.1%/year vs. -16%/year, P < 0.001).ConclusionsConcomitant hepatic steatosis was significantly associated with poor response to treatment in AIH patients. Routine CAP measurements are therefore essential to guide the management of AIH
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