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Found 13 papers, 3 papers with code
EnSiam: Self-Supervised Learning With Ensemble Representations
Recently, contrastive self-supervised learning, where the proximity of representations is determined based on the identities of samples, has made remarkable progress in unsupervised representation learning.
Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations
We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths.
Differentiable Forward and Backward Fixed-Point Iteration Layers
FPI\_NN is intuitive, simple, and fast to train, while FPI_GD can be used for efficient training of energy networks that have been recently studied.
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture.
Neuro-Optimization: Learning Objective Functions Using Neural Networks
With a carefully-designed objective function, mathematical optimization can be quite helpful in solving many problems.
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
In this paper, we propose a knowledge transfer method via distillation of activation boundaries formed by hidden neurons.
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier.
Deep Pose Consensus Networks
In this paper, to resolve this issue, we propose a multiple-partial-hypothesis-based framework for the problem of estimating 3D human pose from a single image, which can be fine-tuned in an end-to-end fashion.
Consensus of Non-Rigid Reconstructions
Recently, there have been many progresses for the problem of non-rigid structure reconstruction based on 2D trajectories, but it is still challenging to deal with complex deformations or restricted view ranges.
Membership Representation for Detecting Block-Diagonal Structure in Low-Rank or Sparse Subspace Clustering
The proposed method shares the philosophy of the above subspace clustering methods, in that it is a self-expressive system based on a Hadamard product of a membership matrix.
Elastic-Net Regularization of Singular Values for Robust Subspace Learning
The proposed method is applied to a number of low-rank matrix approximation problems to demonstrate its efficiency in the presence of heavy corruptions and to show its effectiveness and robustness compared to the existing methods.
A Procrustean Markov Process for Non-Rigid Structure Recovery
Recovering a non-rigid 3D structure from a series of 2D observations is still a difficult problem to solve accurately.
Procrustean Normal Distribution for Non-rigid Structure from Motion
Non-rigid structure from motion is a fundamental problem in computer vision, which is yet to be solved satisfactorily.
