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Found 16 papers, 8 papers with code
Revisiting Confidence Estimation: Towards Reliable Failure Prediction
Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications.
Open-world Machine Learning: A Review and New Outlooks
This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm, to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
Unified Classification and Rejection: A One-versus-All Framework
Previous methods mostly take post-training score transformation or hybrid models to ensure low scores on OOD inputs while separating known classes.
Towards Trustworthy Dataset Distillation
By distilling both InD samples and outliers, the condensed datasets are capable to train models competent in both InD classification and OOD detection.
Adaptive Sparse Pairwise Loss for Object Re-Identification
To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks.
OpenMix: Exploring Outlier Samples for Misclassification Detection
Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications.
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables
However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the performance.
Rethinking Confidence Calibration for Failure Prediction
We investigate this problem and reveal that popular confidence calibration methods often lead to worse confidence separation between correct and incorrect samples, making it more difficult to decide whether to trust a prediction or not.
Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection
Detecting Out-of-distribution (OOD) inputs have been a critical issue for neural networks in the open world.
Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach
To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions.
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
Class-Incremental Learning via Dual Augmentation
Deep learning systems typically suffer from catastrophic forgetting of past knowledge when acquiring new skills continually.
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization
Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues and improve adversarial robustness on different datasets.
Space-Time Distillation for Video Super-Resolution
In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one.
Light Field Super-Resolution With Zero-Shot Learning
To fill this gap, we propose a zero-shot learning framework for light field SR, which learns a mapping to super-resolve the reference view with examples extracted solely from the input low-resolution light field itself.
Multi-Perspective Inferrer: Reasoning Sentences Relationship from Holistic Perspective
Intuitively, NLI should rely more on multiple perspectives to form a holistic view to eliminate bias.
