Search Results for author:
Found 20 papers, 11 papers with code
Neural Stereoscopic Image Style Transfer
While each view of the stereoscopic pair is processed in an individual path, a novel feature aggregation strategy is proposed to effectively share information between the two paths.
Conditional Adversarial Generative Flow for Controllable Image Synthesis
Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from weak ability for conditional image synthesis, especially for multi-label or unaware conditions.
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks.
Ranked #16 on
Image Generation
on STL-10
FasterSeg: Searching for Faster Real-time Semantic Segmentation
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.
Ranked #1 on
Semantic Segmentation
on BDD
Neural Architecture Search
Real-Time Semantic Segmentation
+1
AutoSpeech: Neural Architecture Search for Speaker Recognition
Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often built with off-the-shelf backbones such as VGG-Net or ResNet.
Ranked #6 on
Speaker Identification
on VoxCeleb1
(using extra training data)
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data.
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
We present Sandwich Batch Normalization (SaBN), a frustratingly easy improvement of Batch Normalization (BN) with only a few lines of code changes.
Ranked #20 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-100
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
Can we select the best neural architectures without involving any training and eliminate a drastic portion of the search cost?
SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization
We demonstrate that plugging SAFIN into the base network of another state-of-the-art method results in enhanced stylization.
Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation.
Searching for Two-Stream Models in Multivariate Space for Video Recognition
We design a multivariate search space, including 6 search variables to capture a wide variety of choices in designing two-stream models.
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search
Efficient video architecture is the key to deploying video recognition systems on devices with limited computing resources.
Unified Implicit Neural Stylization
Representing visual signals by implicit representation (e. g., a coordinate based deep network) has prevailed among many vision tasks.
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
Advanced deep neural networks (DNNs), designed by either human or AutoML algorithms, are growing increasingly complex.
NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes
Our framework, called NeRF with Self-supervised Object Segmentation NeRF-SOS, couples object segmentation and neural radiance field to segment objects in any view within a scene.
MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education.
