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Found 21 papers, 8 papers with code
Evolutionary Topology Search for Tensor Network Decomposition
Tensor network (TN) decomposition is a promising framework to represent extremely high-dimensional problems with few parameters.
tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem.
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models
Data augmentation has become a standard component of vision pre-trained models to capture the invariance between augmented views.
What Can Simple Arithmetic Operations Do for Temporal Modeling?
We conduct comprehensive ablation studies on the instantiation of ATMs and demonstrate that this module provides powerful temporal modeling capability at a low computational cost.
Ranked #4 on
Action Recognition
on Something-Something V1
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation
Particularly, in the classification down-stream tasks with linear probes, our proposed method outperforms the state-of-the-art instance-wise and prototypical contrastive learning methods on the ImageNet-100 dataset by 2. 96% and the ImageNet-1K dataset by 2. 46% under the same settings of batch size and epochs.
Design of the topology for contrastive visual-textual alignment
Cosine similarity is the common choice for measuring the distance between the feature representations in contrastive visual-textual alignment learning.
Siamese Prototypical Contrastive Learning
One of the drawbacks of CSL is that the loss term requires a large number of negative samples to provide better mutual information bound ideally.
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
In this study, we focus on transferring knowledge for video classification tasks.
Ranked #1 on
Action Recognition
on ActivityNet
Representation Disentanglement in Generative Models with Contrastive Learning
Recent works apply the contrastive learning on the discriminator of the Generative Adversarial Networks, and there exists little work on exploring if contrastive learning can be applied on encoders to learn disentangled representations.
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning
In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.
On the Memory Mechanism of Tensor-Power Recurrent Models
Tensor-power (TP) recurrent model is a family of non-linear dynamical systems, of which the recurrence relation consists of a p-fold (a. k. a., degree-p) tensor product.
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
We address a problem of estimating pose of a person's head from its RGB image.
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
In this paper, we study design of deep neural networks for tasks of image restoration.
Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
We demonstrate this by compressing the convolutional layers via randomly-shuffled tensor decomposition (RsTD) for a standard classification task using CIFAR-10.
Feature Quantization for Defending Against Distortion of Images
In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion.
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis.
HyperNetworks with statistical filtering for defending adversarial examples
This problem was addressed by employing several defense methods for detection and rejection of particular types of attacks.
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs
In this work, we address the problem of improvement of robustness of feature representations learned using convolutional neural networks (CNNs) to image deformation.
Linear Discriminant Generative Adversarial Networks
We develop a novel method for training of GANs for unsupervised and class conditional generation of images, called Linear Discriminant GAN (LD-GAN).
Information Potential Auto-Encoders
In order to estimate the entropy of the encoding variables and the mutual information, we propose a non-parametric method.
Design of Kernels in Convolutional Neural Networks for Image Classification
Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned representations is limited.
