| Nome |
# |
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Il Futuro della Cybersecurity in Italia: Ambiti Progettuali Strategici, file e383531c-2a31-15e8-e053-a505fe0a3de9
|
839
|
|
From source to target and back: Symmetric Bi-Directional Adaptive GAN, file e383531a-7e71-15e8-e053-a505fe0a3de9
|
140
|
|
Adaptive Deep Learning through Visual Domain Localization, file e383531a-2bb2-15e8-e053-a505fe0a3de9
|
128
|
|
Towards a quantitative measure of rareness, file e3835315-fa3a-15e8-e053-a505fe0a3de9
|
119
|
|
(DE)(CO)-C-2: Deep Depth Colorization, file e383531d-3e17-15e8-e053-a505fe0a3de9
|
105
|
|
Bridging Between Computer and Robot Vision Through Data Augmentation: A Case Study on Object Recognition, file e3835318-5746-15e8-e053-a505fe0a3de9
|
101
|
|
Best Sources Forward: Domain Generalization through Source-Specific Nets, file e383531d-a915-15e8-e053-a505fe0a3de9
|
85
|
|
Robust Place Categorization With Deep Domain Generalization, file e383531d-a911-15e8-e053-a505fe0a3de9
|
79
|
|
Overview of the CLEF 2009 medical image annotation track, file e3835315-ea9b-15e8-e053-a505fe0a3de9
|
78
|
|
Boosting Domain Adaptation by Discovering Latent Domains, file e383531d-9ac7-15e8-e053-a505fe0a3de9
|
78
|
|
When Naïve bayes nearest neighbors meet convolutional neural networks, file e3835316-0c4d-15e8-e053-a505fe0a3de9
|
74
|
|
Towards learning free naive bayes nearest neighbor-based domain adaptation, file e3835316-0dee-15e8-e053-a505fe0a3de9
|
67
|
|
Transfer learning through greedy subset selection, file e3835316-04c7-15e8-e053-a505fe0a3de9
|
66
|
|
Beyond dataset bias: Multi-task unaligned shared knowledge transfer, file e3835316-0f34-15e8-e053-a505fe0a3de9
|
66
|
|
A deep representation for depth images from synthetic data, file e3835326-f79d-15e8-e053-a505fe0a3de9
|
61
|
|
Learning the roots of visual domain shift, file e3835316-1bc9-15e8-e053-a505fe0a3de9
|
52
|
|
Domain generalization with domain-specific aggregation modules, file e3835324-9f15-15e8-e053-a505fe0a3de9
|
48
|
|
Towards Multi-source Adaptive Semantic Segmentation, file d8c751f9-a986-46f0-b13f-b9c5fe7c661a
|
30
|
|
Learning categories from few examples with multi model knowledge transfer, file e3835315-ea9d-15e8-e053-a505fe0a3de9
|
28
|
|
Multi-source adaptive learning for fast control of prosthetics hand, file e3835316-2424-15e8-e053-a505fe0a3de9
|
13
|
|
Classification of hand movements in amputated subjects by sEMG and accelerometers, file e3835312-df56-15e8-e053-a505fe0a3de9
|
12
|
|
Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know, file e3835322-6bbe-15e8-e053-a505fe0a3de9
|
11
|
|
Natural control capabilities of robotic hands by hand amputated subjects, file e3835312-df58-15e8-e053-a505fe0a3de9
|
8
|
|
Hallucinating Agnostic Images to Generalize Across Domains, file 695a4ff8-e63b-4ad3-bc20-7519db94c90f
|
7
|
|
Learning to Learn New Models of Human Activities in Indoor Settings, file e3835316-25d3-15e8-e053-a505fe0a3de9
|
7
|
|
The more you know, the less you learn: From knowledge transfer to one-shot learning of object categories, file e3835315-fd30-15e8-e053-a505fe0a3de9
|
6
|
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Improving control of dexterous hand prostheses using adaptive learning, file e3835315-eaa0-15e8-e053-a505fe0a3de9
|
5
|
|
Multiclass latent locally linear support vector machines, file e3835315-fbdb-15e8-e053-a505fe0a3de9
|
5
|
|
Hallucinating Agnostic Images to Generalize Across Domains, file 4bf7c56f-d797-43f0-af1a-7b72fd686234
|
4
|
|
Leveraging over prior knowledge for online learning of visual categories, file e3835315-c830-15e8-e053-a505fe0a3de9
|
4
|
|
An SVM confidence-based approach to medical image annotation, file e3835315-f7cc-15e8-e053-a505fe0a3de9
|
4
|
|
Multiclass transfer learning from unconstrained priors, file e3835315-fd31-15e8-e053-a505fe0a3de9
|
4
|
|
Adaptive Deep Learning through Visual Domain Localization, file e383531c-5b95-15e8-e053-a505fe0a3de9
|
4
|
|
Melanoma recognition using representative and discriminative kernel classifiers, file e3835315-c831-15e8-e053-a505fe0a3de9
|
3
|
|
Discriminative cue integration for medical image annotation, file e3835315-fa3d-15e8-e053-a505fe0a3de9
|
3
|
|
Frustratingly easy NBNN domain adaptation, file e3835316-0476-15e8-e053-a505fe0a3de9
|
3
|
|
Learning to learn, from transfer learning to domain adaptation: A unifying perspective, file e3835315-cf8e-15e8-e053-a505fe0a3de9
|
2
|
|
Scene recognition with naive bayes non-linear learning, file e3835316-04c8-15e8-e053-a505fe0a3de9
|
2
|
|
Cue Integration for medical image annotation, file e3835316-1086-15e8-e053-a505fe0a3de9
|
2
|
|
Using object affordances to improve object recognition, file e3835316-f9c7-15e8-e053-a505fe0a3de9
|
2
|
|
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work, file e383531a-17a2-15e8-e053-a505fe0a3de9
|
2
|
|
A Deeper Look at Dataset Bias, file e383531a-1e33-15e8-e053-a505fe0a3de9
|
2
|
|
Modeling the background for incremental learning in semantic segmentation, file 34f02060-fb37-4aa4-a8d1-9d28fb4484ad
|
1
|
|
Characterization of a benchmark database for myoelectric movement classification, file e3835312-e1ea-15e8-e053-a505fe0a3de9
|
1
|
|
Kernel methods for melanoma recognition, file e3835316-2425-15e8-e053-a505fe0a3de9
|
1
|
|
From source to target and back: Symmetric Bi-Directional Adaptive GAN, file e383531e-d9d6-15e8-e053-a505fe0a3de9
|
1
|
|
Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know, file e3835322-6bbd-15e8-e053-a505fe0a3de9
|
1
|
|
The Difficulty of Recognizing Grasps from Semg During Activities of Daily Living, file e3835323-6eb1-15e8-e053-a505fe0a3de9
|
1
|
|
Where are we after five editions? Robot Vision Challenge, a Competition that Evaluates Solutions for the Visual Place Classification Problem, file e3835323-a6a2-15e8-e053-a505fe0a3de9
|
1
|
|
AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs, file e3835324-2614-15e8-e053-a505fe0a3de9
|
1
|
|
Adding new tasks to a single network with weight transformations using binary masks, file e3835324-97f5-15e8-e053-a505fe0a3de9
|
1
|
|
Domain generalization with domain-specific aggregation modules, file e3835324-9cda-15e8-e053-a505fe0a3de9
|
1
|
|
Domain generalization by solving jigsaw puzzles, file e3835324-a492-15e8-e053-a505fe0a3de9
|
1
|
|
Adding new tasks to a single network with weight transformations using binary masks, file e3835324-abd1-15e8-e053-a505fe0a3de9
|
1
|
|
Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency, file e3835324-ad50-15e8-e053-a505fe0a3de9
|
1
|
| Totale |
2.372 |