289 research outputs found
Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation
Deep Reinforcement Learning (DRL) has shown great potential in enabling robots to find certain objects (e.g., `find a fridge') in environments like homes or schools. This task is known as Object-Goal Navigation (ObjectNav). DRL methods are predominantly trained and evaluated using environment simulators. Although DRL has shown impressive results, the simulators may be biased or limited. This creates a risk of shortcut learning, i.e., learning a policy tailored to specific visual details of training environments. We aim to deepen our understanding of shortcut learning in ObjectNav, its implications and propose a solution. We design an experiment for inserting a shortcut bias in the appearance of training environments. As a proof-of-concept, we associate room types to specific wall colors (e.g., bedrooms with green walls), and observe poor generalization of a state-of-the-art (SOTA) ObjectNav method to environments where this is not the case (e.g., bedrooms with blue walls). We find that shortcut learning is the root cause: the agent learns to navigate to target objects, by simply searching for the associated wall color of the target object's room. To solve this, we propose Language-Based (L-B) augmentation. Our key insight is that we can leverage the multimodal feature space of a Vision-Language Model (VLM) to augment visual representations directly at the feature-level, requiring no changes to the simulator, and only an addition of one layer to the model. Where the SOTA ObjectNav method's success rate drops 69%, our proposal has only a drop of 23%
Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation
Deep Reinforcement Learning (DRL) has shown great potential in enabling
robots to find certain objects (e.g., `find a fridge') in environments like
homes or schools. This task is known as Object-Goal Navigation (ObjectNav). DRL
methods are predominantly trained and evaluated using environment simulators.
Although DRL has shown impressive results, the simulators may be biased or
limited. This creates a risk of shortcut learning, i.e., learning a policy
tailored to specific visual details of training environments. We aim to deepen
our understanding of shortcut learning in ObjectNav, its implications and
propose a solution. We design an experiment for inserting a shortcut bias in
the appearance of training environments. As a proof-of-concept, we associate
room types to specific wall colors (e.g., bedrooms with green walls), and
observe poor generalization of a state-of-the-art (SOTA) ObjectNav method to
environments where this is not the case (e.g., bedrooms with blue walls). We
find that shortcut learning is the root cause: the agent learns to navigate to
target objects, by simply searching for the associated wall color of the target
object's room. To solve this, we propose Language-Based (L-B) augmentation. Our
key insight is that we can leverage the multimodal feature space of a
Vision-Language Model (VLM) to augment visual representations directly at the
feature-level, requiring no changes to the simulator, and only an addition of
one layer to the model. Where the SOTA ObjectNav method's success rate drops
69%, our proposal has only a drop of 23%.Comment: 8 pages, 6 figures, to be published in IEEE IRC 202
Differentiated thyroid carcinoma : treatment and clinical consequences of therapy
The first chapters of this thesis describe the treatment of radioiodine non-avid thyroid carcinoma with the tyrosine kinase inhibitor sorafenib. The remainder of the thesis describes the clinical consequences of the treatment of thyroid carcinoma.Bayer B.V. Novo Nordisk B.V. Servier Nederland Farma B.V. MSD B.V. Genzyme B.V. AstraZeneca B.V. Ipsen Farmaceutica B.V. Novartis Pharma B.V. J.E. Jurriaanse StichtingUBL - phd migration 201
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