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Found 62 papers, 25 papers with code
Instance-hiding Schemes for Private Distributed Learning
The new ideas in the current paper are: (a) new variants of mixup with negative as well as positive coefficients, and extend the sample-wise mixup to be pixel-wise.
Designs, Motion Mechanism, Motion Coordination, and Communication of Bionic Robot Fishes: A Survey
Various methods are needed to narrow the gap in swimming performance between robot fishes and fish.
Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications
The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback.
Recovering Private Text in Federated Learning of Language Models
In this paper, we present a novel attack method FILM for federated learning of language models -- for the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences.
Fast Few-shot Debugging for NLU Test Suites
We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem.
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling.
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis.
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Gradient inversion attack (or input recovery from gradient) is an emerging threat to the security and privacy preservation of Federated learning, whereby malicious eavesdroppers or participants in the protocol can recover (partially) the clients' private data.
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning
Buffer cache has been widely implemented in database systems to reduce disk I/Os.
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
CarNet: A Lightweight and Efficient Encoder-Decoder Architecture for High-quality Road Crack Detection
In this paper, we present a lightweight encoder-decoder architecture, CarNet, for efficient and high-quality crack detection.
EMA: Auditing Data Removal from Trained Models
In this paper, we propose a new method called Ensembled Membership Auditing (EMA) for auditing data removal to overcome these limitations.
Plot2Spectra: an Automatic Spectra Extraction Tool
In scientific literature, XANES/Raman data are usually plotted in line graphs which is a visually appropriate way to represent the information when the end-user is a human reader.
Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function
Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance.
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network
They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors.
Ranked #2 on
Super-Resolution
on IXI
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit Assignment
In this paper, we propose a cooperative MARL method with sequential credit assignment (SeCA) that deduces each agent's contribution to the team's success one by one to learn better cooperation.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain.
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Contrasted with prior work, this paper provides a complementary solution to align domains by learning the same auxiliary tasks in both domains simultaneously.
L2E: Learning to Exploit Your Opponent
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
VINS: Visual Search for Mobile User Interface Design
By utilizing this dataset, we propose an object-detection based image retrieval framework that models the UI context and hierarchical structure.
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.
Temperature Regret Matching for Imperfect-Information Games
Regret matching (RM) plays a crucial role in CFR and its variants to approach Nash equilibrium.
Video Matting via Consistency-Regularized Graph Neural Networks
In this paper, we propose to enhance the temporal coherence by Consistency-Regularized Graph Neural Networks (CRGNN) with the aid of a synthesized video matting dataset.
Neighbor Class Consistency on Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data in a target domain with labeled data from source domain available.
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio
To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization
In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words.
Pushing The Limit of Type I Codebook For FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach
There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems.
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks.
TextHide: Tackling Data Privacy in Language Understanding Tasks
In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e. g., BERT) for any sentence or sentence-pair task.
InstaHide: Instance-hiding Schemes for Private Distributed Learning
This paper introduces InstaHide, a simple encryption of training images, which can be plugged into existing distributed deep learning pipelines.
Light Field View Synthesis via Aperture Disparity and Warping Confidence Map
This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images.
Cross-Domain Document Object Detection: Benchmark Suite and Method
We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation.
Adversarial Feature Hallucination Networks for Few-Shot Learning
The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data.
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility.
Learning Relaxed Belady for Content Distribution Network Caching
This paper presents a new approach for caching in CDNs that uses machine learning to approximate the Belady MIN algorithm.
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective
We instead reformulate ZSL as a conditioned visual classification problem, i. e., classifying visual features based on the classifiers learned from the semantic descriptions.
Visual Semantic Reasoning for Image-Text Matching
It outperforms the current best method by 6. 8% relatively for image retrieval and 4. 8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set).
Ranked #7 on
Image Retrieval
on Flickr30K 1K test
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection
A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e. g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV.
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains challenging.
Authenticated Key-Value Stores with Hardware Enclaves
To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Face Anti-spoofing gains increased attentions recently in both academic and industrial fields.
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks.
Fast 3D Line Segment Detection From Unorganized Point Cloud
This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Support Neighbor Loss for Person Re-Identification
To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets.
Payoff Control in the Iterated Prisoner's Dilemma
Repeated game has long been the touchstone model for agents' long-run relationships.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
To solve these problems, we propose the very deep residual channel attention networks (RCAN).
Ranked #11 on
Image Super-Resolution
on BSD100 - 4x upscaling
Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
Deep Ordinal Hashing with Spatial Attention
However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.
Large Linear Multi-output Gaussian Process Learning
Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach.
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes.
Quantum cluster approach to the topological invariants in correlated Chern insulators
We detect the topological properties of Chern insulators with strong Coulomb interactions by use of cluster perturbation theory and variational cluster approach.
Strongly Correlated Electrons
From rules to runs: A dynamic epistemic take on imperfect information games
We argue that the problem lies in the mix-up of two interpretations of the extensive form game structures: game rules or game runs which do not always coincide.
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos
With the prevalence of the commodity depth cameras, the new paradigm of user interfaces based on 3D motion capturing and recognition have dramatically changed the way of interactions between human and computers.
Line-Based Multi-Label Energy Optimization for Fisheye Image Rectification and Calibration
Fisheye image rectification and estimation of intrinsic parameters for real scenes have been addressed in the literature by using line information on the distorted images.
Rank Subspace Learning for Compact Hash Codes
In this work, we propose a novel hash learning framework that encodes feature's rank orders instead of numeric values in a number of optimal low-dimensional ranking subspaces.
