Identifying Out-of-distribution (OOD) data is becoming increasingly critical
as the real-world applications of deep learning methods expand. Post-hoc
methods modify softmax scores fine-tuned on outlier data or leverage
intermediate feature layers to identify distinctive patterns between
In-Distribution (ID) and OOD samples. Other methods focus on employing diverse
OOD samples to learn discrepancies between ID and OOD. These techniques,
however, are typically dependent on the quality of the outlier samples assumed.
Density-based methods explicitly model class-conditioned distributions but this
requires long training time or retraining the classifier. To tackle these
issues, we introduce \textit{FlowCon}, a new density-based OOD detection
technique. Our main innovation lies in efficiently combining the properties of
normalizing flow with supervised contrastive learning, ensuring robust
representation learning with tractable density estimation. Empirical evaluation
shows the enhanced performance of our method across common vision datasets such
as CIFAR-10 and CIFAR-100 pretrained on ResNet18 and WideResNet classifiers. We
also perform quantitative analysis using likelihood plots and qualitative
visualization using UMAP embeddings and demonstrate the robustness of the
proposed method under various OOD contexts. Code will be open-sourced post
decision.

通过将常态化流与监督对比学习相结合，我们引入了一种新的基于密度的离群检测技术，名为 FlowCon，确保了具有可跟踪密度估计的鲁棒表征学习。经验证明我们的方法在常见的视觉数据集上表现出卓越的性能，如 CIFAR-10 和 CIFAR-100 的 ResNet18 和 WideResNet 分类器经过预训练。我们还通过似然性图进行了定量分析，并使用 UMAP 嵌入进行了定性可视化，证明了所提方法在各种 OOD 情况下的稳健性。

FlowCon：使用基于流对比学习的异常检测

FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive  Learning

Neural Radiance Fields (NeRFs) have revolutionized the field of novel view
synthesis, demonstrating remarkable performance. However, the modeling and
rendering of reflective objects remain challenging problems. Recent methods
have shown significant improvements over the baselines in handling reflective
scenes, albeit at the expense of efficiency. In this work, we aim to strike a
balance between efficiency and quality. To this end, we investigate an
implicit-explicit approach based on conventional volume rendering to enhance
the reconstruction quality and accelerate the training and rendering processes.
We adopt an efficient density-based grid representation and reparameterize the
reflected radiance in our pipeline. Our proposed reflection-aware approach
achieves a competitive quality efficiency trade-off compared to competing
methods. Based on our experimental results, we propose and discuss hypotheses
regarding the factors influencing the results of density-based methods for
reconstructing reflective objects. The source code is available at:
this https URL

我们研究了一种基于隐式 - 显式方法的密度场重建和渲染的方案，以在处理反射场景时在效率和质量之间取得均衡，并提出了关于影响重建反射对象结果的因素的假设。