Search Results for author:
Found 13 papers, 8 papers with code
Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization
Synthetic defect data generation is a popular approach for mitigating data challenges.
TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights
In this work, we propose that the optimal data for DPO has equal expected rewards for each token in winning and losing responses, as there is no difference in token importance.
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance.
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation
In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF.
VeCLIP: Improving CLIP Training via Visual-enriched Captions
For example, VeCLIP achieves up to +25. 2% gain in COCO and Flickr30k retrieval tasks under the 12M setting.
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted.
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation
To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation
On active learning task, our method achieves 97. 0% Top-1 Accuracy on CIFAR10 with 0. 1% annotated data, and 83. 9% Top-1 Accuracy on CIFAR100 with 10% annotated data.
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space.
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning
We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL).
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
SUOD: Toward Scalable Unsupervised Outlier Detection
In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models.
