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Found 17 papers, 3 papers with code
Many-to-many Image Generation with Auto-regressive Diffusion Models
This paper introduces a domain-general framework for many-to-many image generation, capable of producing interrelated image series from a given set of images, offering a scalable solution that obviates the need for task-specific solutions across different multi-image scenarios.
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures
In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures.
Generating Molecular Conformer Fields
In this paper we tackle the problem of generating conformers of a molecule in 3D space given its molecular graph.
Pseudo-Generalized Dynamic View Synthesis from a Video
In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.
Manifold Diffusion Fields
Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold.
Diffusion Probabilistic Fields
Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains.
A Dot Product Attention Free Transformer
We introduce Dot Product Attention Free Transformer (DAFT), an efficient variant of Transformers \citep{transformer} that eliminates the query-key dot product in self attention.
Ranked #623 on
Image Classification
on ImageNet
Fast and Explicit Neural View Synthesis
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.
Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks
Deep linear networks trained with gradient descent yield low rank solutions, as is typically studied in matrix factorization.
Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks
We analyze the learning dynamics of infinitely wide neural networks with a finite sized bottle-neck.
Unconstrained Scene Generation with Locally Conditioned Radiance Fields
In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera.
Ranked #1 on
Scene Generation
on VizDoom
Uncertainty Weighted Offline Reinforcement Learning
Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.
On the generalization of learning-based 3D reconstruction
State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training.
Adversarial Fisher Vectors for Unsupervised Representation Learning
In contrast to a traditional view where the discriminator learns a constant function when reaching convergence, here we show that it can provide useful information for downstream tasks, e. g., feature extraction for classification.
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Deep neural networks require collecting and annotating large amounts of data to train successfully.
Ranked #44 on
Self-Supervised Action Recognition
on UCF101
Hierarchical Bayes Autoencoders
During inference time, the HBAE consists of two sampling steps: first a latent code for the input is sampled, and then this code is passed to the conditional generator to output a stochastic reconstruction.
