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Found 20 papers, 12 papers with code
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
We demonstrate that this result is independent of the similarity between the training and testing data and applies to well-known families of neural network architectures for VQA (i. e. monolithic architectures and neural module networks).
Modularity Trumps Invariance for Compositional Robustness
In this work we develop a compositional image classification task where, given a few elemental corruptions, models are asked to generalize to compositions of these corruptions.
HICO-DET-SG and V-COCO-SG: New Data Splits for Evaluating the Systematic Generalization Performance of Human-Object Interaction Detection Models
Human-Object Interaction (HOI) detection is a task to localize humans and objects in an image and predict the interactions in human-object pairs.
Human-Object Interaction Detection
Systematic Generalization
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios.
Safe Exploration Method for Reinforcement Learning under Existence of Disturbance
We define the safety during learning as satisfaction of the constraint conditions explicitly defined in terms of the state and propose a safe exploration method that uses partial prior knowledge of a controlled object and disturbance.
Transformer Module Networks for Systematic Generalization in Visual Question Answering
We reveal that Neural Module Networks (NMNs), i. e., question-specific compositions of modules that tackle a sub-task, achieve better or similar systematic generalization performance than the conventional Transformers, even though NMNs' modules are CNN-based.
Do Neural Networks for Segmentation Understand Insideness?
Only recurrent networks trained with small images learn solutions that generalize well to almost any curve.
Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections
Symmetry is omnipresent in nature and perceived by the visual system of many species, as it facilitates detecting ecologically important classes of objects in our environment.
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available.
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood.
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Datasets often contain input dimensions that are unnecessary to predict the output label, e. g. background in object recognition, which lead to more trainable parameters.
Adversarial examples within the training distribution: A widespread challenge
This result provides evidence supporting theories attributing adversarial examples to the proximity of data to ground-truth class boundaries, and calls into question other theories which do not account for this more stringent definition of adversarial attacks.
How Modular Should Neural Module Networks Be for Systematic Generalization?
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task.
Two-step reinforcement learning for model-free redesign of nonlinear optimal regulator
To overcome this limitation, we propose a model-free two-step design approach that improves the transient learning performance of RL in an optimal regulator redesign problem for unknown nonlinear systems.
Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfaction
In reinforcement learning (RL) algorithms, exploratory control inputs are used during learning to acquire knowledge for decision making and control, while the true dynamics of a controlled object is unknown.
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations
We find that learning category and viewpoint in separate networks compared to a shared one leads to an increase in selectivity and invariance, as separate networks are not forced to preserve information about both category and viewpoint.
The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions
In this paper, we investigate the impact of unnecessary input dimensions on one of the central issues of machine learning: the number of training examples needed to achieve high generalization performance, which we refer to as the network's data efficiency.
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space
We show our method has the following properties: (i) the Jacobian matrix between the input space and a Euclidean latent space forms a constantlyscaled orthonormal system and enables isometric data embedding; (ii) the relation of PDFs in both spaces can become tractable one such as proportional relation.
Do Deep Neural Networks for Segmentation Understand Insideness?
At the heart of image segmentation lies the problem of determining whether a pixel is inside or outside a region, which we denote as the "insideness" problem.
