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Found 40 papers, 18 papers with code
REACTO: Reconstructing Articulated Objects from a Single Video
In this paper, we address the challenge of reconstructing general articulated 3D objects from a single video.
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
UniSSDA is at the intersection of Universal Domain Adaptation (UniDA) and Semi-Supervised Domain Adaptation (SSDA): the UniDA setting does not allow for fine-grained categorization of target private classes not represented in the source domain, while SSDA focuses on the restricted closed-set setting where source and target label spaces match exactly.
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior
In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model.
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data
The impressive performances of large language models (LLMs) and their immense potential for commercialization have given rise to serious concerns over the intellectual property (IP) of their training data.
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation
The conventional in-domain calibration method, \textit{temperature scaling} (TempScal), encounters challenges due to domain distribution shifts and the absence of labeled target domain data.
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance.
Fair yet Asymptotically Equal Collaborative Learning
In collaborative learning with streaming data, nodes (e. g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data.
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes.
Fourier Sensitivity and Regularization of Computer Vision Models
Using this result, we propose a general measure of any differentiable model's Fourier-sensitivity using the unitary Fourier-transform of its input-gradient.
Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation
We propose to distil useful target domain information through a co-learning strategy to improve target pseudolabel quality for finetuning the source model.
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
Surprisingly, we find that vanilla Global MP performs very well against the SOTA techniques.
Domain Generalization via Selective Consistency Regularization for Time Series Classification
Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training.
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
Recent test-time adaptation methods update batch normalization layers of pre-trained source models deployed in new target environments with streaming data to mitigate such performance degradation.
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation
In this work, we propose SemiCurv, a semi-supervised learning (SSL) framework for curvilinear structure segmentation that is able to utilize such unlabelled data to reduce the labelling burden.
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
Ranked #1 on
3D Point Cloud Data Augmentation
on ModelNet40
On Representation Knowledge Distillation for Graph Neural Networks
Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings.
Global Magnitude Pruning With Minimum Threshold Is All We Need
We showcase that magnitude based pruning, specifically, global magnitude pruning (GP) is sufficient to achieve SOTA performance on a range of neural network architectures.
Spatial Frequency Sensitivity Regularization for Robustness
The ability to generalize to out-of-distribution data is a major challenge for modern deep neural networks.
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data
Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data.
Source-Free Few-Shot Domain Adaptation
Test-time adaptation of pre-trained source models with streaming unlabelled target data is an attractive setting that protects the privacy of source data, but it has mini-batch size and class-distribution requirements on the streaming data which might not be desirable in practice.
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization
Domain generalization aims to learn models robust to domain shift, with limited source domains at training and without any access to target domain samples except at test time.
Diverse and Consistent Multi-view Networks for Semi-supervised Regression
Label collection is costly in many applications, which poses the need for label-efficient learning.
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking.
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis
In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation.
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
In this paper, we propose ARMOURED, an adversarially robust training method based on semi-supervised learning that consists of two components.
Empirical Analysis of Overfitting and Mode Drop in GAN Training
We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective.
Classify and Generate: Using Classification Latent Space Representations for Image Generations
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area.
Scalable and Practical Natural Gradient for Large-Scale Deep Learning
Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size.
Learning to Impute: A General Framework for Semi-supervised Learning
Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.
Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks.
Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials
We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties.
Holistic Multi-modal Memory Network for Movie Question Answering
In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.
Manifold regularization with GANs for semi-supervised learning
Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
The Unusual Effectiveness of Averaging in GAN Training
We examine two different techniques for parameter averaging in GAN training.
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
GANS are powerful generative models that are able to model the manifold of natural images.
Efficient multiple hyperparameter learning for log-linear models
Using multiple regularization hyperparameters is an effective method for managing model complexity in problems where input features have varying amounts of noise.
