Search Results for author:
Found 11 papers, 9 papers with code
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
The deterministic nature of the existing finetuning methods makes them overlook the many possible interactions across the modalities and deems them unsafe for high-risk CL tasks requiring reliable uncertainty estimation.
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects.
The Neural Process Family: Survey, Applications and Perspectives
We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM).
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation
With experiments on the WMT20 chat translation task dataset, we demonstrate that NMT confusion networks can help to reduce the perplexity of both n-gram and recurrent neural network LMs compared to those trained only on N-best translations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis
This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain.
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization
Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes well to other tasks such as human activity recognition (HAR).
Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages
Out-of-vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for low-resource language (LRL) pairs, i. e., language pairs for which few or no parallel corpora exist.
Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction
The ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits in Morphologically Rich Languages (MRLs) a notoriously complicated task.
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
We show that the Inception+SVM model establishes a state-of-the-art F1 score on the task of gender recognition of cartoon faces.
