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Found 51 papers, 19 papers with code
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
Spot-adaptive Knowledge Distillation
Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks.
Mining and searching association relation of scientific papers based on deep learning
There is a complex correlation among the data of scientific papers.
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers
in the heterogeneous graph of scientific papers.
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.
Meta-attention for ViT-backed Continual Learning
Continual learning is a longstanding research topic due to its crucial role in tackling continually arriving tasks.
Knowledge Amalgamation for Object Detection with Transformers
Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence.
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN
The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction.
gDNA: Towards Generative Detailed Neural Avatars
Furthermore, we show that our method can be used on the task of fitting human models to raw scans, outperforming the previous state-of-the-art.
Up to 100$\times$ Faster Data-free Knowledge Distillation
At the heart of our approach is a novel strategy to reuse the shared common features in training data so as to synthesize different data instances.
Auto-Tag: Tagging-Data-By-Example in Data Lakes
As data lakes become increasingly popular in large enterprises today, there is a growing need to tag or classify data assets (e. g., files and databases) in data lakes with additional metadata (e. g., semantic column-types), as the inferred metadata can enable a range of downstream applications like data governance (e. g., GDPR compliance), and dataset search.
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers
The aggregate gradient strategy is a versatile module for mainstream CNN classifiers.
Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training
Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV).
A Survey of Deep Learning for Low-Shot Object Detection
Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task.
Safe Distillation Box
In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model.
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
Human Performance Capture from Monocular Video in the Wild
In this work, we propose a method capable of capturing the dynamic 3D human shape from a monocular video featuring challenging body poses, without any additional input.
Render In-between: Motion Guided Video Synthesis for Action Interpolation
We propose to address these issues in a motion-guided frame-upsampling framework that is capable of producing realistic human motion and appearance.
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Knowledge distillation~(KD) aims to craft a compact student model that imitates the behavior of a pre-trained teacher in a target domain.
Shape-aware Multi-Person Pose Estimation from Multi-View Images
In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images.
Distribution Knowledge Embedding for Graph Pooling
A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.
Tree-Like Decision Distillation
Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.
Contrastive Model Inversion for Data-Free Knowledge Distillation
In this paper, we propose Contrastive Model Inversion~(CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue.
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation
Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs).
Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes
Complex data pipelines are increasingly common in diverse applications such as BI reporting and ML modeling.
DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network
In this paper, we argue that a Mutual Nearest Neighbor (MNN) relation should be established to explicitly select the query descriptors that are most relevant to each task and discard less relevant ones from aggregative clutters in FSL.
Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network
The proposed STGCN utilizes graph convolution to integrate network topology information into the learning model to exploit spatial information.
Training Generative Adversarial Networks in One Stage
Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort.
EM-POSE: 3D Human Pose Estimation From Sparse Electromagnetic Trackers
To this end, we present a method to estimate SMPL parameters from 6-12 EM sensors.
Self-Born Wiring for Neural Trees
Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter.
Progressive Network Grafting for Few-Shot Knowledge Distillation
In this paper, we investigate the practical few-shot knowledge distillation scenario, where we assume only a few samples without human annotations are available for each category.
Convolutional Autoencoders for Human Motion Infilling
At the heart of our approach lies the idea to cast motion infilling as an inpainting problem and to train a convolutional de-noising autoencoder on image-like representations of motion sequences.
Human Body Model Fitting by Learned Gradient Descent
We propose a novel algorithm for the fitting of 3D human shape to images.
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances.
Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images
In this way, rich image appearance models together with more contextual information are integrated by learning a series of decision tree ensembles.
Impression Space from Deep Template Network
This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression.
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability
In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs.
Data-Free Adversarial Distillation
Knowledge Distillation (KD) has made remarkable progress in the last few years and become a popular paradigm for model compression and knowledge transfer.
Deep Model Transferability from Attribution Maps
Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter.
Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation
To this end, we introduce a dual-step strategy that first extracts the task-specific knowledge from the heterogeneous teachers sharing the same sub-task, and then amalgamates the extracted knowledge to build the student network.
Investigation of wind pressures on tall building under interference effects using machine learning techniques
This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming.
Unpaired Pose Guided Human Image Generation
This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance or type of outfit worn by the user.
Monocular Neural Image Based Rendering with Continuous View Control
The approach is self-supervised and only requires 2D images and associated view transforms for training.
End-to-end Learning for Graph Decomposition
The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels of the initial graph and the hard constraints are introduced in the CRF as high-order potentials.
Amalgamating Knowledge towards Comprehensive Classification
We propose in this paper to study a new model-reusing task, which we term as \emph{knowledge amalgamation}.
Selective Zero-Shot Classification with Augmented Attributes
We propose a selective zero-shot classifier based on both the human defined and the automatically discovered residual attributes.
Transductive Unbiased Embedding for Zero-Shot Learning
Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen (source) classes.
Cross-modal Deep Variational Hand Pose Estimation
Furthermore, we show that our proposed method can be used without changes on depth images and performs comparably to specialized methods.
Learning Human Motion Models for Long-term Predictions
Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists.
Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
Temporal information can provide additional cues about the location of body joints and help to alleviate these issues.
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Pose Estimation
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