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Found 30 papers, 14 papers with code
Meta-Auxiliary Learning for Micro-Expression Recognition
Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection.
Intriguing Properties of Positional Encoding in Time Series Forecasting
Motivated by these findings, we introduce two new PEs: Temporal Position Encoding (T-PE) for temporal tokens and Variable Positional Encoding (V-PE) for variable tokens.
Self-Supervised Representation Learning with Meta Comprehensive Regularization
Specifically, we update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features.
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
From the perspective of distribution analyses, we disclose that the intrinsic issues behind the phenomenon are the over-multitudinous conceptual knowledge contained in PLMs and the abridged knowledge for target downstream domains, which jointly result in that PLMs mis-locate the knowledge distributions corresponding to the target domains in the universal knowledge embedding space.
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
To this end, we propose a novel hierarchical topology isomorphism expertise embedded graph contrastive learning, which introduces knowledge distillations to empower GCL models to learn the hierarchical topology isomorphism expertise, including the graph-tier and subgraph-tier.
Hacking Task Confounder in Meta-Learning
However, our experiments reveal an unexpected result: there is negative knowledge transfer between tasks, affecting generalization performance.
Background Debiased SAR Target Recognition via Causal Interventional Regularizer
Based on the constructed SCM, we propose a causal intervention based regularization method to eliminate the negative impact of background on feature semantic learning and achieve background debiased SAR-ATR.
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning
The long-term goal of machine learning is to learn general visual representations from a small amount of data without supervision, mimicking three advantages of human cognition: i) no need for labels, ii) robustness to data scarcity, and iii) learning from experience.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning.
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
Specifically, 1) we maximize the MI between visual and semantic space for distribution alignment; 2) we leverage the temporal information for estimating the MI by encouraging MI to increase as more frames are observed.
Spatio-Temporal Branching for Motion Prediction using Motion Increments
Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses.
Towards Task Sampler Learning for Meta-Learning
We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty.
Towards the Sparseness of Projection Head in Self-Supervised Learning
In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data.
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models
In response, we propose a plug-and-play method named Manifold Guidance Sampling, which is also the first unsupervised method to mitigate bias issue in DDPMs.
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning
To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks.
Manifold Constraint Regularization for Remote Sensing Image Generation
This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on remote sensing images. To address this challenge, this paper analyzes the characteristics of remote sensing images and proposes manifold constraint regularization, a novel approach that tackles overfitting of GANs on remote sensing images for the first time.
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information.
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance.
Rethinking skip connection model as a learnable Markov chain
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity
To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views.
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model.
Robust Causal Graph Representation Learning against Confounding Effects
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
Interventional Contrastive Learning with Meta Semantic Regularizer
Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner.
Supporting Vision-Language Model Inference with Causality-pruning Knowledge Prompt
Vision-language models are pre-trained by aligning image-text pairs in a common space so that the models can deal with open-set visual concepts by learning semantic information from textual labels.
MetAug: Contrastive Learning via Meta Feature Augmentation
We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder.
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation
We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain.
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness.
Domain-Invariant Representation Learning with Global and Local Consistency
In this paper, we give an analysis of the existing representation learning framework of unsupervised domain adaptation and show that the learned feature representations of the source domain samples are with discriminability, compressibility, and transferability.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.
