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Found 61 papers, 26 papers with code
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
In this work, we provide a finetuning approach to enhance the robustness of vision transformers inspired by the concept of nullspace from linear algebra.
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation
Starting with the class of interest, we query the LLMs to extract relevant knowledge for these novel domains.
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare.
GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
Our system of different roles will leverage this knowledge graph to generate new jailbreaks, which have proved effective in inducing LLMs to generate unethical or guideline-violating responses.
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Despite advances in AI alignment, language models (LM) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries modify input prompts to induce harmful behavior.
ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers
Automated diagnosis of Alzheimer Disease(AD) from brain imaging, such as magnetic resonance imaging (MRI), has become increasingly important and has attracted the community to contribute many deep learning methods.
Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style
Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage.
Dataset Distillation via the Wasserstein Metric
Dataset Distillation (DD) emerges as a powerful strategy to encapsulate the expansive information of large datasets into significantly smaller, synthetic equivalents, thereby preserving model performance with reduced computational overhead.
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
Our method focuses on producing SVGs that are both accurate and simple, aligning with human readability and understanding.
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
In this paper, we introduce a novel approach, namely, Selective Cross-Modality Distillation for Domain Generalization (SCMD).
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation.
Ranked #13 on
Domain Generalization
on ImageNet-Sketch
Adaptive Test-Time Personalization for Federated Learning
To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains.
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents.
Ranked #7 on
Code Generation
on MBPP
Understanding Adversarial Transferability in Federated Learning
We investigate the robustness and security issues from a novel and practical setting: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients, and only revealing their adversary position after the training to conduct transferable adversarial attacks with their data, which is usually a subset of the data that FL system is trained with.
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance
Domain generalization studies the problem of training a model with samples from several domains (or distributions) and then testing the model with samples from a new, unseen domain.
Ranked #15 on
Domain Generalization
on PACS
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion.
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Accordingly, this survey presents the background of trustworthy machine learning development using a unified set of concepts, connects this language to Pearl's causal hierarchy, and finally discusses methods explicitly inspired by causality literature.
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized.
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
By leveraging the XML-based textual descriptions of SVG representations instead of raster images, we aim to bridge the gap between the visual and textual modalities, allowing LLMs to directly understand and manipulate images without the need for parameterized visual components.
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
To address the challenge posed by BadLabel, we further propose a robust LNL method that perturbs the labels in an adversarial manner at each epoch to make the loss values of clean and noisy labels again distinguishable.
Calibrated Teacher for Sparsely Annotated Object Detection
Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations.
Iterative Few-shot Semantic Segmentation from Image Label Text
Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images.
Ranked #40 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks.
Ranked #1 on
Classifier calibration
on CIFAR-100
Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection
As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the computed tomography image based AI diagnostic system has become a sustainable solution for early diagnosis.
A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets
Deep Neural Network (DNN) models are increasingly evaluated using new replication test datasets, which have been carefully created to be similar to older and popular benchmark datasets.
MRCLens: an MRC Dataset Bias Detection Toolkit
Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data.
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning
We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective.
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution
With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions.
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Finally, we test this simple technique we identify (worst-case data augmentation with squared l2 norm alignment regularization) and show that the benefits of this method outrun those of the specially designed methods.
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.
The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.
Measure and Improve Robustness in NLP Models: A Survey
Despite robustness being an increasingly studied topic, it has been separately explored in applications like vision and NLP, with various definitions, evaluation and mitigation strategies in multiple lines of research.
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies
Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS.
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Machine learning has demonstrated remarkable prediction accuracy over i. i. d data, but the accuracy often drops when tested with data from another distribution.
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
We also show that model bias favors texture and shape features differently under different test settings.
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
Deep learning based subtomogram classification have played critical roles for such tasks.
Enabling the Network to Surf the Internet
Few-shot learning is challenging due to the limited data and labels.
On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Learning Robust Models by Countering Spurious Correlations
In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label.
Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Word Shape Matters: Robust Machine Translation with Visual Embedding
Neural machine translation has achieved remarkable empirical performance over standard benchmark datasets, yet recent evidence suggests that the models can still fail easily dealing with substandard inputs such as misspelled words, To overcome this issue, we introduce a new encoding heuristic of the input symbols for character-level NLP models: it encodes the shape of each character through the images depicting the letters when printed.
Self-Challenging Improves Cross-Domain Generalization
We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data.
Ranked #27 on
Domain Generalization
on PACS
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients
Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns.
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
We first learn a biased model that only uses features that are known to relate to dataset bias.
Learning Robust Global Representations by Penalizing Local Predictive Power
Despite their renowned predictive power on i. i. d.
Ranked #104 on
Domain Generalization
on PACS
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).
Learning Robust Representations by Projecting Superficial Statistics Out
We test our method on the battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training.
Ranked #113 on
Domain Generalization
on PACS
What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks
In this paper, we further investigate the statistical irregularities, what we refer as confounding factors, of the NLI data sets.
Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis.
On the Origin of Deep Learning
This paper is a review of the evolutionary history of deep learning models.
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data
In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms.
Select-Additive Learning: Improving Generalization in Multimodal Sentiment Analysis
In this paper, we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained neural networks for multimodal sentiment analysis.
Evaluating Protein-protein Interaction Predictors with a Novel 3-Dimensional Metric
In order for the predicted interactions to be directly adopted by biologists, the ma- chine learning predictions have to be of high precision, regardless of recall.
A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas
Further, we will also look into the development history of modelling time series data with neural networks.
Evaluation of Protein-protein Interaction Predictors with Noisy Partially Labeled Data Sets
In this paper, we focused on the problem that non-availability of accurately labeled testing data sets in the domain of protein-protein interaction (PPI) prediction may lead to biased evaluation results.
Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition
We propose a transfer deep learning (TDL) framework that can transfer the knowledge obtained from a single-modal neural network to a network with a different modality.
Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
However, none of existing approaches addresses the problem of identifying network-wide important crossroads in real road network.
