Search Results for author:
Found 18 papers, 10 papers with code
Continual Learning in the Presence of Repetition
Continual learning (CL) provides a framework for training models in ever-evolving environments.
Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions
We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i. e. test time) when the respective weather conditions are encountered.
An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions
This catastrophic forgetting is typically addressed via incremental learning approaches which usually re-train the model by either keeping a memory bank of training samples or keeping a copy of the entire model or model parameters for each scenario.
On the importance of cross-task features for class-incremental learning
We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
Class-incremental learning: survey and performance evaluation on image classification
For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning.
Disentanglement of Color and Shape Representations for Continual Learning
We hypothesize that disentangled feature representations suffer less from catastrophic forgetting.
On Class Orderings for Incremental Learning
The influence of class orderings in the evaluation of incremental learning has received very little attention.
Ternary Feature Masks: zero-forgetting for task-incremental learning
To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization.
A continual learning survey: Defying forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
LIUM-CVC Submissions for WMT18 Multimodal Translation Task
This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation.
Metric Learning for Novelty and Anomaly Detection
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.
Context Proposals for Saliency Detection
Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation.
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios.
Domain-adaptive deep network compression
We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing.
LIUM-CVC Submissions for WMT17 Multimodal Translation Task
This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation.
Does Multimodality Help Human and Machine for Translation and Image Captioning?
This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge.
On-the-fly Network Pruning for Object Detection
Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image.
