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Found 35 papers, 17 papers with code
Bayesian Deep Learning Approach for Real-time Lane-based Arrival Curve Reconstruction at Intersection using License Plate Recognition Data
In this study, we propose a Bayesian deep learning approach for real-time lane-based arrival curve reconstruction, in which the lane choice patterns and uncertainties of link-based arrivals are both characterized.
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption.
UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration
We propose a novel unsupervised image registration method named the unified Transformer and superresolution (UTSRMorph) network, which can enhance feature representation learning in the encoder and generate detailed displacement fields in the decoder to overcome these problems.
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?
This high within-class similarity can be attributed to the fact that previous methods use samples from different classes to construct a single batch for batch normalization (BN) matching.
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition
We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity.
Data-independent Module-aware Pruning for Hierarchical Vision Transformers
Hierarchical vision transformers (ViTs) have two advantages over conventional ViTs.
Multisize Dataset Condensation
These two challenges connect to the "subset degradation problem" in traditional dataset condensation: a subset from a larger condensed dataset is often unrepresentative compared to directly condensing the whole dataset to that smaller size.
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.
You Only Condense Once: Two Rules for Pruning Condensed Datasets
However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) the limited computational resources often preclude the possibility of conducting additional condensation processes.
Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain
While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms.
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing
In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks.
Structured Pruning for Deep Convolutional Neural Networks: A survey
The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs.
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets.
A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery
In this study, we proposed a Parameter-Free Non-Convex Tensor Completion model (TC-PFNC) for traffic data recovery, in which a log-based relaxation term was designed to approximate tensor algebraic rank.
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction.
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction.
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data.
Automated Deepfake Detection
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
The rapid advances in deep generative models over the past years have led to highly {realistic media, known as deepfakes,} that are commonly indistinguishable from real to human eyes.
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization
Despite notable progress in gradient compression, the existing quantization methods require further improvement when low-bits compression is applied, especially the overall systems often degenerate a lot when quantization are applied in double directions to compress model weights and gradients.
Synthetic Convolutional Features for Improved Semantic Segmentation
Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss.
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time.
Progressive Local Filter Pruning for Image Retrieval Acceleration
To verify this, we evaluate our method on two widely-used image retrieval datasets, i. e., Oxford5k and Paris6K, and one person re-identification dataset, i. e., Market-1501.
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation
Today's success of state of the art methods for semantic segmentation is driven by large datasets.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
Filter Pruning by Switching to Neighboring CNNs with Good Attributes
In addition, when evaluating the filter importance, only the magnitude information of the filters is considered.
Stochastic Model Pruning via Weight Dropping Away and Back
Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights.
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small.
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks
With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable.
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
Therefore, the network trained by our method has a larger model capacity to learn from the training data.
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images.
Learning Dilation Factors for Semantic Segmentation of Street Scenes
In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective.
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information.
STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling
The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene.
Ranked #104 on
Semantic Segmentation
on NYU Depth v2
