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Found 13 papers, 9 papers with code
Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models
Safety alignment is the key to guiding the behaviors of large language models (LLMs) that are in line with human preferences and restrict harmful behaviors at inference time, but recent studies show that it can be easily compromised by finetuning with only a few adversarially designed training examples.
Interactive Visual Learning for Stable Diffusion
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention.
LLM Attributor: Interactive Visual Attribution for LLM Generation
Our library offers a new way to quickly attribute an LLM's text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text.
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
We present UniTable, a training framework that unifies both the training paradigm and training objective of TR.
Self-Supervised Pre-Training for Table Structure Recognition Transformer
We discover that the performance gap between the linear projection transformer and the hybrid CNN-transformer can be mitigated by SSP of the visual encoder in the TSR model.
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
This allows it to "see" an appropriate portion of the table and "store" the complex table structure within sufficient context length for the subsequent transformer.
Ranked #4 on
Table Recognition
on PubTabNet
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs.
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
We test LLM Self Defense on GPT 3. 5 and Llama 2, two of the current most prominent LLMs against various types of attacks, such as forcefully inducing affirmative responses to prompts and prompt engineering attacks.
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations, enabling users to fluidly transition between multiple levels of abstraction through animations and interactive elements.
RobArch: Designing Robust Architectures against Adversarial Attacks
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs).
IMB-NAS: Neural Architecture Search for Imbalanced Datasets
In this paper, we propose a new and complementary direction for improving performance on long tailed datasets - optimizing the backbone architecture through neural architecture search (NAS).
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors
With deep learning based systems performing exceedingly well in many vision-related tasks, a major concern with their widespread deployment especially in safety-critical applications is their susceptibility to adversarial attacks.
Accurate Anchor Free Tracking
Specifically, a target object is defined by a bounding box center, tracking offset, and object size.
