Trending Research
Thin-Plate Spline Motion Model for Image Animation
yoyo-nb/thin-plate-spline-motion-model
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Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image.
OPT: Open Pre-trained Transformer Language Models
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning.
Ranked #1 on
Stereotypical Bias Analysis
on CrowS-Pairs
Extracting Triangular 3D Models, Materials, and Lighting From Images
NVlabs/nvdiffrec
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We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
GLU Variants Improve Transformer
BlinkDL/RWKV-LM
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Gated Linear Units (arXiv:1612. 08083) consist of the component-wise product of two linear projections, one of which is first passed through a sigmoid function.
HeadNeRF: A Real-time NeRF-based Parametric Head Model
Different from existing related parametric models, we use the neural radiance fields as a novel 3D proxy instead of the traditional 3D textured mesh, which makes that HeadNeRF is able to generate high fidelity images.
Fine-Tuning Language Models from Human Preferences
lvwerra/trl
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Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.
The Primacy Bias in Deep Reinforcement Learning
evgenii-nikishin/rl_with_resets
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This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later.
Ranked #2 on
Atari Games 100k
on Atari 100k
ConvMAE: Masked Convolution Meets Masked Autoencoders
alpha-vl/convmae
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Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer architectures can further unleash the potentials of ViT, leading to state-of-the-art performances on image classification, detection and semantic segmentation.
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems
shyamsn97/controllable-ncas
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Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of "growing" artificial cells into images, 3D structures, and even functional machines.
DeepDPM: Deep Clustering With an Unknown Number of Clusters
Using a split/merge framework, a dynamic architecture that adapts to the changing K, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones).
Ranked #6 on
Unsupervised Image Classification
on ImageNet
