2.3k citations · 3.1k across the 21 of their papers we have counts for
21 papers
Hyperbolic Contrastive Learning for Visual Representations beyond Objects
Songwei Ge, Shlok Mishra, Simon Kornblith +2
Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this pap…
Decoupling Local and Global Representations of Time Series
Sana Tonekaboni, Chun-Liang Li, Sercan Arik +2
Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a bett…
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li +2
We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike…
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
Songwei Ge, Shlok Mishra, Haohan Wang +2
Unsupervised learning has recently made exceptional progress because of the development of more effective contrastive learning methods. However, CNNs are prone to depend on low-lev…
Object-aware Contrastive Learning for Debiased Scene Representation
Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn +2
Contrastive self-supervised learning has shown impressive results in learning visual representations from unlabeled images by enforcing invariance against different data augmentati…
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Chu Wang +5
Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial…
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li +3
Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued…
Learning and Evaluating Representations for Deep One-class Classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon +2
We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on lear…
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Yuliang Zou, Zizhao Zhang, Han Zhang +4
Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accu…
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn +3
Contrastive representation learning has shown to be effective to learn representations from unlabeled data. However, much progress has been made in vision domains relying on data a…
A Simple Semi-Supervised Learning Framework for Object Detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li +3
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data. Although there has been remarkable recent prog…
Learned Interpolation for 3D Generation
Austin Dill, Songwei Ge, Eunsu Kang +2
In order to generate novel 3D shapes with machine learning, one must allow for interpolation. The typical approach for incorporating this creative process is to interpolate in a le…
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li +6
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combin…
Getting Topology and Point Cloud Generation to Mesh
Austin Dill, Chun-Liang Li, Songwei Ge +1
In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface,…
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan O. Arik +3
Human explanations of high-level decisions are often expressed in terms of key concepts the decisions are based on. In this paper, we study such concept-based explainability for De…
Developing Creative AI to Generate Sculptural Objects
Songwei Ge, Austin Dill, Eunsu Kang +4
We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical detai…
Hallucinating Point Cloud into 3D Sculptural Object
Chun-Liang Li, Eunsu Kang, Songwei Ge +4
Our team of artists and machine learning researchers designed a creative algorithm that can generate authentic sculptural artworks. These artworks do not mimic any given forms and…
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
Haoran You, Yu Cheng, Tianheng Cheng +2
Recent techniques built on Generative Adversarial Networks (GANs), such as Cycle-Consistent GANs, are able to learn mappings among different domains built from unpaired datasets, t…
Point Cloud GAN
Chun-Liang Li, Manzil Zaheer, Yang Zhang +2
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightfo…
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
Xi Ouyang, Yu Cheng, Yifan Jiang +2
State-of-the-art pedestrian detection models have achieved great success in many benchmarks. However, these models require lots of annotation information and the labeling process u…
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu +2
We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic)…