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Found 18 papers, 4 papers with code
Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset
To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts.
ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer
Deep learning models achieve high accuracy in segmentation tasks among others, yet domain shift often degrades the models' performance, which can be critical in real-world scenarios where no target images are available.
Marginal Thresholding in Noisy Image Segmentation
Based on recent work on loss function characterization, it is shown that optimal solutions to soft-Dice can be recovered by thresholding solutions to cross-entropy with a particular a priori unknown threshold that efficiently can be computed.
Noisy Image Segmentation With Soft-Dice
This paper presents a study on the soft-Dice loss, one of the most popular loss functions in medical image segmentation, for situations where noise is present in target labels.
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces
This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA).
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via strong data augmentation.
Towards a Unified View of Affinity-Based Knowledge Distillation
In this paper, by explicitly modularising knowledge distillation into a framework of three components, i. e. affinity, normalisation, and loss, we give a unified treatment of these algorithms as well as study a number of unexplored combinations of the modules.
An analysis of over-sampling labeled data in semi-supervised learning with FixMatch
Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches.
Discriminant analysis based on projection onto generalized difference subspace
To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion.
Regularizing CNN Transfer Learning with Randomised Regression
That is, a CNN is efficiently regularized without additional resources of data or prior domain expertise.
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Training a deep object detector for autonomous driving requires a huge amount of labeled data.
Target Aware Network Adaptation for Efficient Representation Learning
This paper presents an automatic network adaptation method that finds a ConvNet structure well-suited to a given target task, e. g., image classification, for efficiency as well as accuracy in transfer learning.
A multitask deep learning model for real-time deployment in embedded systems
We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems.
A systematic study of the class imbalance problem in convolutional neural networks
In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities.
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior.
Visual Instance Retrieval with Deep Convolutional Networks
This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval.
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.
Factors of Transferability for a Generic ConvNet Representation
In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target).
