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08/29/2023
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Unlike cloud-based deep learning models that are often large and uniform...
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08/18/2023
TrOMR:Transformer-Based Polyphonic Optical Music Recognition
Optical Music Recognition (OMR) is an important technology in music and ...
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08/09/2023
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
Novel Class Discovery (NCD) aims at inferring novel classes in an unlabe...
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06/15/2023
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operati...
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06/15/2023
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Modern machine learning models deployed in the wild can encounter both c...
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06/09/2023
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?
Recent large vision-language models such as CLIP have shown remarkable o...
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06/01/2023
Genetic Algorithms for Searching a Matrix of Metagrammars for Synthesis
Syntax-guided synthesis is a paradigm in program synthesis in which the ...
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05/22/2023
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection
Out-of-distribution (OOD) detection is a critical task for reliable pred...
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05/05/2023
A Survey on Out-of-Distribution Detection in NLP
Out-of-distribution (OOD) detection is essential for the reliable and sa...
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03/23/2023
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
This work studies the generalization issue of face anti-spoofing (FAS) m...
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03/10/2023
Distributionally Robust Optimization with Probabilistic Group
Modern machine learning models may be susceptible to learning spurious c...
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03/06/2023
Non-Parametric Outlier Synthesis
Out-of-distribution (OOD) detection is indispensable for safely deployin...
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03/02/2023
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections
Place recognition is an important technique for autonomous cars to achie...
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02/28/2023
BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images
Place recognition is a key module for long-term SLAM systems. Current Li...
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12/08/2022
Logit Clipping for Robust Learning against Label Noise
In the presence of noisy labels, designing robust loss functions is crit...
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11/24/2022
Delving into Out-of-Distribution Detection with Vision-Language Representations
Recognizing out-of-distribution (OOD) samples is critical for machine le...
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10/26/2022
Is Out-of-Distribution Detection Learnable?
Supervised learning aims to train a classifier under the assumption that...
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10/13/2022
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine ...
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10/07/2022
Novice Type Error Diagnosis with Natural Language Models
Strong static type systems help programmers eliminate many errors withou...
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09/27/2022
A critical appraisal of equity in conversational AI: Evidence from auditing GPT-3's dialogues with different publics on climate change and Black Lives Matter
Autoregressive language models, which use deep learning to produce human...
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08/18/2022
Out-of-distribution Detection via Frequency-regularized Generative Models
Modern deep generative models can assign high likelihood to inputs drawn...
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08/04/2022
Open-world Contrastive Learning
Recent advance in contrastive learning has shown remarkable performance....
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07/26/2022
Task Agnostic and Post-hoc Unseen Distribution Detection
Despite the recent advances in out-of-distribution(OOD) detection, anoma...
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06/28/2022
POEM: Out-of-Distribution Detection with Posterior Sampling
Out-of-distribution (OOD) detection is indispensable for machine learnin...
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05/19/2022
Mitigating Neural Network Overconfidence with Logit Normalization
Detecting out-of-distribution inputs is critical for safe deployment of ...
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04/13/2022
Out-of-distribution Detection with Deep Nearest Neighbors
Out-of-distribution (OOD) detection is a critical task for deploying mac...
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03/17/2022
Are Vision Transformers Robust to Spurious Correlations?
Deep neural networks may be susceptible to learning spurious correlation...
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03/08/2022
CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is a critical task for reliable mach...
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03/08/2022
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild
Building reliable object detectors that can detect out-of-distribution (...
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02/07/2022
Training OOD Detectors in their Natural Habitats
Out-of-distribution (OOD) detection is important for machine learning mo...
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02/02/2022
VOS: Learning What You Don't Know by Virtual Outlier Synthesis
Out-of-distribution (OOD) detection has received much attention lately d...
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01/26/2022
Interactive Image Inpainting Using Semantic Guidance
Image inpainting approaches have achieved significant progress with the ...
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01/22/2022
PiCO: Contrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each tr...
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12/01/2021
Provable Guarantees for Understanding Out-of-distribution Detection
Out-of-distribution (OOD) detection is important for deploying machine l...
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11/24/2021
ReAct: Out-of-distribution Detection With Rectified Activations
Out-of-distribution (OOD) detection has received much attention lately d...
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11/18/2021
On the Effectiveness of Sparsification for Detecting the Deep Unknowns
Detecting out-of-distribution (OOD) inputs is a central challenge for sa...
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10/26/2021
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Machine learning models often encounter samples that are diverged from t...
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10/21/2021
Generalized Out-of-Distribution Detection: A Survey
Out-of-distribution (OOD) detection is critical to ensuring the reliabil...
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10/18/2021
Natural Attribute-based Shift Detection
Despite the impressive performance of deep networks in vision, language,...
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10/01/2021
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Detecting out-of-distribution (OOD) data has become a critical component...
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09/29/2021
Can multi-label classification networks know what they don't know?
Estimating out-of-distribution (OOD) uncertainty is a central challenge ...
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09/12/2021
On the Impact of Spurious Correlation for Out-of-distribution Detection
Modern neural networks can assign high confidence to inputs drawn from o...
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05/16/2021
MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
Spatio-temporal action detection is an important and challenging problem...
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05/05/2021
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Detecting out-of-distribution (OOD) inputs is a central challenge for sa...
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04/30/2021
MOOD: Multi-level Out-of-distribution Detection
Out-of-distribution (OOD) detection is essential to prevent anomalous in...
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10/08/2020
Energy-based Out-of-distribution Detection
Determining whether inputs are out-of-distribution (OOD) is an essential...
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08/15/2020
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Classifiers in machine learning are often brittle when deployed. Particu...
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07/06/2020
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
We present a multi-agent actor-critic method that aims to implicitly add...
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01/14/2020
Actions as Moving Points
The existing action tubelet detectors mainly depend on heuristic anchor ...
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03/05/2019