research
∙
07/06/2023
LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias
Textual noise, such as typos or abbreviations, is a well-known issue tha...
share
research
∙
07/05/2022
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching
Product matching is a fundamental step for the global understanding of c...
research
∙
02/17/2022
Domain Randomization for Object Counting
Recently, the use of synthetic datasets based on game engines has been s...
research
∙
10/27/2021
How Important is Importance Sampling for Deep Budgeted Training?
Long iterative training processes for Deep Neural Networks (DNNs) are co...
research
∙
10/26/2021
Addressing out-of-distribution label noise in webly-labelled data
A recurring focus of the deep learning community is towards reducing the...
research
∙
12/08/2020
Multi-Objective Interpolation Training for Robustness to Label Noise
Deep neural networks trained with standard cross-entropy loss memorize n...
research
∙
11/15/2020
Unsupervised Contrastive Learning of Sound Event Representations
Self-supervised representation learning can mitigate the limitations in ...
research
∙
07/23/2020
ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning
Reducing the amount of labels required to trainconvolutional neural netw...
research
∙
12/18/2019
Towards Robust Learning with Different Label Noise Distributions
Noisy labels are an unavoidable consequence of automatic image labeling ...
research
∙
08/08/2019
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
Semi-supervised learning, i.e. jointly learning from labeled an unlabele...
research
∙
04/25/2019
On guiding video object segmentation
This paper presents a novel approach for segmenting moving objects in un...
research
∙
04/25/2019