b'Yixuan Li'

research

∙ 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...

0 Wenxing Xu, et al. ∙

research

∙ 08/18/2023

TrOMR:Transformer-Based Polyphonic Optical Music Recognition

Optical Music Recognition (OMR) is an important technology in music and ...

0 Yixuan Li, et al. ∙

research

∙ 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...

0 Yiyou Sun, et al. ∙

research

∙ 06/15/2023

OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

Out-of-Distribution (OOD) detection is critical for the reliable operati...

0 Jingyang Zhang, et al. ∙

research

∙ 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...

0 Haoyue Bai, et al. ∙

research

∙ 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...

0 Yifei Ming, et al. ∙

research

∙ 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 ...

0 Yixuan Li, et al. ∙

research

∙ 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...

0 Rheeya Uppaal, et al. ∙

research

∙ 05/05/2023

A Survey on Out-of-Distribution Detection in NLP

Out-of-distribution (OOD) detection is essential for the reliable and sa...

0 Hao Lang, et al. ∙

research

∙ 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...

0 Yiyou Sun, et al. ∙

research

∙ 03/10/2023

Distributionally Robust Optimization with Probabilistic Group

Modern machine learning models may be susceptible to learning spurious c...

0 Soumya Suvra Ghosal, et al. ∙

research

∙ 03/06/2023

Non-Parametric Outlier Synthesis

Out-of-distribution (OOD) detection is indispensable for safely deployin...

0 Leitian Tao, et al. ∙

research

∙ 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...

0 Yixuan Li, et al. ∙

research

∙ 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...

0 Lun Luo, et al. ∙

research

∙ 12/08/2022

Logit Clipping for Robust Learning against Label Noise

In the presence of noisy labels, designing robust loss functions is crit...

0 Hongxin Wei, et al. ∙

research

∙ 11/24/2022

Delving into Out-of-Distribution Detection with Vision-Language Representations

Recognizing out-of-distribution (OOD) samples is critical for machine le...

0 Yifei Ming, et al. ∙

research

∙ 10/26/2022

Is Out-of-Distribution Detection Learnable?

Supervised learning aims to train a classifier under the assumption that...

0 Zhen Fang, et al. ∙

research

∙ 10/13/2022

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection

Out-of-distribution (OOD) detection is vital to safety-critical machine ...

0 Jingkang Yang, et al. ∙

research

∙ 10/07/2022

Novice Type Error Diagnosis with Natural Language Models

Strong static type systems help programmers eliminate many errors withou...

0 Chuqin Geng, et al. ∙

research

∙ 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...

0 Kaiping Chen, et al. ∙

research

∙ 08/18/2022

Out-of-distribution Detection via Frequency-regularized Generative Models

Modern deep generative models can assign high likelihood to inputs drawn...

21 Mu Cai, et al. ∙

research

∙ 08/04/2022

Open-world Contrastive Learning

Recent advance in contrastive learning has shown remarkable performance....

0 Yiyou Sun, et al. ∙

research

∙ 07/26/2022

Task Agnostic and Post-hoc Unseen Distribution Detection

Despite the recent advances in out-of-distribution(OOD) detection, anoma...

0 Radhika Dua, et al. ∙

research

∙ 06/28/2022

POEM: Out-of-Distribution Detection with Posterior Sampling

Out-of-distribution (OOD) detection is indispensable for machine learnin...

0 Yifei Ming, et al. ∙

research

∙ 05/19/2022

Mitigating Neural Network Overconfidence with Logit Normalization

Detecting out-of-distribution inputs is critical for safe deployment of ...

0 Hongxin Wei, et al. ∙

research

∙ 04/13/2022

Out-of-distribution Detection with Deep Nearest Neighbors

Out-of-distribution (OOD) detection is a critical task for deploying mac...

0 Yiyou Sun, et al. ∙

research

∙ 03/17/2022

Are Vision Transformers Robust to Spurious Correlations?

Deep neural networks may be susceptible to learning spurious correlation...

0 Soumya Suvra Ghosal, et al. ∙

research

∙ 03/08/2022

CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution Detection

Out-of-distribution (OOD) detection is a critical task for reliable mach...

0 Yifei Ming, et al. ∙

research

∙ 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 (...

106 Xuefeng Du, et al. ∙

research

∙ 02/07/2022

Training OOD Detectors in their Natural Habitats

Out-of-distribution (OOD) detection is important for machine learning mo...

0 Julian Katz-Samuels, et al. ∙

research

∙ 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...

13 Xuefeng Du, et al. ∙

research

∙ 01/26/2022

Interactive Image Inpainting Using Semantic Guidance

Image inpainting approaches have achieved significant progress with the ...

4 Wangbo Yu, et al. ∙

research

∙ 01/22/2022

PiCO: Contrastive Label Disambiguation for Partial Label Learning

Partial label learning (PLL) is an important problem that allows each tr...

0 Haobo Wang, et al. ∙

research

∙ 12/01/2021

Provable Guarantees for Understanding Out-of-distribution Detection

Out-of-distribution (OOD) detection is important for deploying machine l...

0 Peyman Morteza, et al. ∙

research

∙ 11/24/2021

ReAct: Out-of-distribution Detection With Rectified Activations

Out-of-distribution (OOD) detection has received much attention lately d...

0 Yiyou Sun, et al. ∙

research

∙ 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...

0 Yiyou Sun, et al. ∙

research

∙ 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...

17 Mohammadreza Salehi, et al. ∙

research

∙ 10/21/2021

Generalized Out-of-Distribution Detection: A Survey

Out-of-distribution (OOD) detection is critical to ensuring the reliabil...

17 Jingkang Yang, et al. ∙

research

∙ 10/18/2021

Natural Attribute-based Shift Detection

Despite the impressive performance of deep networks in vision, language,...

2 Jeonghoon Park, et al. ∙

research

∙ 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...

0 Rui Huang, et al. ∙

research

∙ 09/29/2021

Can multi-label classification networks know what they don't know?

Estimating out-of-distribution (OOD) uncertainty is a central challenge ...

0 Haoran Wang, et al. ∙

research

∙ 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...

0 Yifei Ming, et al. ∙

research

∙ 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...

0 Yixuan Li, et al. ∙

research

∙ 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...

0 Rui Huang, et al. ∙

research

∙ 04/30/2021

MOOD: Multi-level Out-of-distribution Detection

Out-of-distribution (OOD) detection is essential to prevent anomalous in...

0 Ziqian Lin, et al. ∙

research

∙ 10/08/2020

Energy-based Out-of-distribution Detection

Determining whether inputs are out-of-distribution (OOD) is an essential...

30 Weitang Liu, et al. ∙

research

∙ 08/15/2020

Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

Classifiers in machine learning are often brittle when deployed. Particu...

18 Karan Goel, et al. ∙

research

∙ 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...

0 Meng Zhou, et al. ∙

research

∙ 01/14/2020

Actions as Moving Points

The existing action tubelet detectors mainly depend on heuristic anchor ...

14 Yixuan Li, et al. ∙

research

∙ 03/05/2019

Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search

A plethora of recent work has shown that convolutional networks are not ...

0 Abhimanyu Dubey, et al. ∙

Yixuan Li

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