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Found 11 papers, 3 papers with code
Value-Aided Conditional Supervised Learning for Offline RL
Offline reinforcement learning (RL) has seen notable advancements through return-conditioned supervised learning (RCSL) and value-based methods, yet each approach comes with its own set of practical challenges.
LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes
Specifically, we project a portion of point cloud to the desired view and provide the projection as a guidance for inpainting using the generative model.
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making
However, we discovered that the attention module of DT is not appropriate to capture the inherent local dependence pattern in trajectories of RL modeled as a Markov decision process.
Deep learning-based statistical noise reduction for multidimensional spectral data
In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired.
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks
Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners.
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
By training a policy on mixture tasks along with original training tasks, LDM allows the agent to prepare for unseen test tasks during training and prevents the agent from overfitting the training tasks.
NTIRE 2021 Challenge on Image Deblurring
In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions.
NTIRE 2021 Challenge on Video Super-Resolution
Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart.
Motion-Aware Dynamic Architecture for Efficient Frame Interpolation
Video frame interpolation aims to synthesize accurate intermediate frames given a low-frame-rate video.
DynaVSR: Dynamic Adaptive Blind Video Super-Resolution
Most conventional supervised super-resolution (SR) algorithms assume that low-resolution (LR) data is obtained by downscaling high-resolution (HR) data with a fixed known kernel, but such an assumption often does not hold in real scenarios.
Ranked #7 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer
JavaScript (JS) engine vulnerabilities pose significant security threats affecting billions of web browsers.
