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Found 39 papers, 9 papers with code
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model
Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow.
An Energy-Based Prior for Generative Saliency
With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction.
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
In this paper, we take a step further by proposing a novel generative vision transformer with latent variables following an informative energy-based prior for salient object detection.
Unsupervised Contrastive Learning for Signal-Dependent Noise Synthesis
We present a simple yet robust noise synthesis framework based on unsupervised contrastive learning.
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel.
Energy-Based Generative Cooperative Saliency Prediction
In this paper, to model the uncertainty of visual saliency, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over the saliency map given an input image, and treating the saliency prediction as a sampling process from the learned distribution.
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning
Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the distribution of patches within the image without relying on external training data.
Learning Deep Latent Variable Models by Short-Run MCMC Inference With Optimal Transport Correction
Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.
Semi-Supervised Video Deraining with Dynamical Rain Generator
Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos.
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation
This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy-based model and a latent variable model.
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling
Energy-based models (EBMs) for generative modeling parametrize a single net and can be directly trained by maximum likelihood estimation.
Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler
In this paper, we propose to learn a variational auto-encoder (VAE) to initialize the finite-step MCMC, such as Langevin dynamics that is derived from the energy function, for efficient amortized sampling of the EBM.
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world.
A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras
The grid cells in the mammalian medial entorhinal cortex exhibit striking hexagon firing patterns when the agent navigates in the open field.
Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection
The proposed model consists of two sub-models parameterized by neural networks: (1) a saliency predictor that maps input images to clean saliency maps, and (2) a noise generator, which is a latent variable model that produces noises from Gaussian latent vectors.
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
In this paper, we conduct theoretical analysis of a general representation model of path integration by grid cells, where the 2D self-position is encoded as a higher dimensional vector, and the 2D self-motion is represented by a general transformation of the vector.
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network.
Representation Learning: A Statistical Perspective
Learning representations of data is an important problem in statistics and machine learning.
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns
To model the motions explicitly, it is natural for the model to be based on the motions or the displacement fields of the pixels.
Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns
We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns.
Learning Pose Grammar for Monocular 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image.
Ranked #11 on
3D Human Pose Estimation
on HumanEva-I
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems.
Energy-Based Continuous Inverse Optimal Control
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations.
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning
This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e. g., the output is a photo image and the input is a sketch image.
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion
This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1).
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time
The non-linear transformation of this transition model can be parametrized by a feedforward neural network.
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Finally, we transform the output of recurrent neural network into the corresponding action distribution.
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion
In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector.
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Ranked #4 on
Human Part Segmentation
on PASCAL-Part
(using extra training data)
Learning Descriptor Networks for 3D Shape Synthesis and Analysis
This paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for modeling volumetric shape patterns.
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e. g., from visible to invisible) and track humans in videos.
Generative Hierarchical Learning of Sparse FRAME Models
This paper proposes a method for generative learning of hierarchical random field models.
Super-Trajectory for Video Segmentation
We introduce a novel semi-supervised video segmentation approach based on an efficient video representation, called as "super-trajectory".
Cooperative Training of Descriptor and Generator Networks
Specifically, within each iteration of the cooperative learning algorithm, the generator model generates initial synthesized examples to initialize a finite-step MCMC that samples and trains the energy-based descriptor model.
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms
Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks.
Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet
We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns.
A Theory of Generative ConvNet
If we further assume that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then we obtain a generative ConvNet model that is unique among energy-based models: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process.
Learning Inhomogeneous FRAME Models for Object Patterns
We investigate an inhomogeneous version of the FRAME (Filters, Random field, And Maximum Entropy) model and apply it to modeling object patterns.
