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Found 12 papers, 4 papers with code
Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations.
Robust Representation Learning via Perceptual Similarity Metrics
A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream task, without overfitting to spurious input features.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
We show that coupling a PointMask layer with an arbitrary model can discern the points in the input space which contribute the most to the prediction score, thereby leading to interpretability.
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.
Signed Input Regularization
To test the effectiveness of the proposed idea and compare it with other competing methods, we design several test scenarios, such as classification performance, uncertainty, out-of-distribution, and robustness analyses.
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class.
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures.
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations.
Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks
Recently, there have been several successful deep learning approaches for automatically classifying chest X-ray images into different disease categories.
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
The output imbalance refers to the imbalance between the false positives and false negatives of the inference model.
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.
