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Found 13 papers, 3 papers with code
Generally-Occurring Model Change for Robust Counterfactual Explanations
Counterfactual explanation is an important method in the field of interpretable machine learning, which can not only help users understand why machine learning models make specific decisions, but also help users understand how to change these decisions.
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms
However, the robustness defined from the perspective of perturbed instances is sometimes biased, because this definition ignores the impact of learning algorithms on robustness.
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space
By employing information fusion techniques, our method maximizes the use of data to address feature counterfactual explanations in the feature space.
Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering
Specifically, we perform perspective projection to render the 3D rational B\'ezier curves into 2D curves, which are subsequently converted to a 2D raster image via our customized differentiable rasterizer.
WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting
Various approaches have been introduced to time series analysis, including both statistical approaches and deep neural networks.
TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization
In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.
Mix-GENEO: A Flexible Filtration for Multiparameter Persistent Homology Detects Digital Images
The experiment results demonstrate our bifiltrations have ability to detect geometric and topological differences of digital images.
P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion
Inferring missing regions from severely occluded point clouds is highly challenging.
TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples without catastrophic forgetting of old classes or overfitting to new classes.
class-incremental learning
Few-Shot Class-Incremental Learning
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P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification
Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification.
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames.
FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs
In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation.
Light Weight Color Image Warping with Inter-Channel Information
Image warping is a necessary step in many multimedia applications such as texture mapping, image-based rendering, panorama stitching, image resizing and optical flow computation etc.
