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Found 24 papers, 7 papers with code
Accurate Cutting-point Estimation for Robotic Lychee Harvesting through Geometry-aware Learning
Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots.
SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning
We thoroughly evaluate our SPTNet on standard benchmarks and demonstrate that our method outperforms existing GCD methods.
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution
Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years.
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention
Recent years have witnessed the great potential of attention mechanism in graph representation learning.
Generalist: Decoupling Natural and Robust Generalization
The parameters of base learners are collected and combined to form a global learner at intervals during the training process.
Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances.
Self-Ensemble Adversarial Training for Improved Robustness
In this work, we are dedicated to the weight states of models through the training process and devise a simple but powerful \emph{Self-Ensemble Adversarial Training} (SEAT) method for yielding a robust classifier by averaging weights of history models.
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
TrafPS consists of three layers, from data process to results computation and visualization.
Adversarial Training using Contrastive Divergence
To protect the security of machine learning models against adversarial examples, adversarial training becomes the most popular and powerful strategy against various adversarial attacks by injecting adversarial examples into training data.
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning
Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by adding visually imperceptible perturbations to the input images.
Distributional Discrepancy: A Metric for Unconditional Text Generation
The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data.
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e. g., the criminals may use the adversarial perturbations to cheat the CCTV systems.
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
To alleviate the exposure bias, generative adversarial networks (GAN) use the discriminator to update the generator's parameters directly, but they fail by being evaluated precisely.
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation.
The Detection of Distributional Discrepancy for Text Generation
In this paper, we theoretically propose two metric functions to measure the distributional difference between real text and generated text.
Multi-local Collaborative AutoEncoder
In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure.
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM).
Crowd Counting using Deep Recurrent Spatial-Aware Network
Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera's perspective that causes huge appearance variations in people's scales and rotations.
Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints
To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints restricted Boltzmann machine with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by pairwise constraints and the process of encoding is conducted under these guidances.
