Search Results for author:
Found 21 papers, 13 papers with code
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
To study this problem, we organized a capture-the-flag competition at IEEE SaTML 2024, where the flag is a secret string in the LLM system prompt.
Robust Concept Erasure Using Task Vectors
Finally, we show that Diverse Inversion enables us to apply a TV edit only to a subset of the model weights, enhancing the erasure capabilities while better maintaining the core functionality of the model.
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass.
Classifying Nodes in Graphs without GNNs
Recently, distillation methods succeeded in eliminating the use of GNNs at test time but they still require them during training.
Set Features for Anomaly Detection
This paper proposes to use set features for detecting anomalies in samples that consist of unusual combinations of normal elements.
Ranked #4 on
Anomaly Detection
on MVTec LOCO AD
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks
Tabular classification has traditionally relied on supervised algorithms, which estimate the parameters of a prediction model using its training data.
Circumventing Concept Erasure Methods For Text-to-Image Generative Models
Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public.
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
It is tempting to hypothesize that anomaly detection can improve indefinitely by increasing the scale of our networks, making their representations more expressive.
Set Features for Fine-grained Anomaly Detection
Fine-grained anomaly detection has recently been dominated by segmentation based approaches.
Ranked #1 on
Anomaly Detection
on UEA time-series datasets
Improving Zero-Shot Models with Label Distribution Priors
We propose a new approach, CLIPPR (CLIP with Priors), which adapts zero-shot models for regression and classification on unlabelled datasets.
Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry.
Ranked #1 on
Anomaly Detection
on ODDS
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute from being considered as relevant for anomaly detection.
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
We propose an architecture for solving PerVL that operates by extending the input vocabulary of a pretrained model with new word embeddings for the new personalized concepts.
Ranked #24 on
Zero-Shot Composed Image Retrieval (ZS-CIR)
on CIRCO
Approaches Toward Physical and General Video Anomaly Detection
The classes differ in the presented phenomena, the normal class variability, and the kind of anomalies in the videos.
Ranked #1 on
Physical Video Anomaly Detection
on PHANTOM
(using extra training data)
Out-of-Distribution Detection Without Class Labels
Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm.
Ranked #1 on
Anomaly Detection
on Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102
(using extra training data)
Language-Guided Image Clustering
In this setting, the model is provided with an exhaustive list of phrases describing all the possible values of a specific attribute, together with a shared image-language embedding (e. g.
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data.
Dataset Summarization by K Principal Concepts
The output of our method is a set of K principal concepts that summarize the dataset.
Ranked #4 on
Image Clustering
on ImageNet-100
(using extra training data)
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation
In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning.
Ranked #1 on
Anomaly Detection
on Cats-and-Dogs
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images.
Ranked #7 on
Anomaly Classification
on GoodsAD
Deep Nearest Neighbor Anomaly Detection
Nearest neighbors is a successful and long-standing technique for anomaly detection.
Ranked #2 on
Anomaly Detection
on Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200
(using extra training data)
