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Found 17 papers, 9 papers with code
Representation Learning via Consistent Assignment of Views over Random Partitions
We extensively ablate our method and demonstrate that our proposed random partition pretext task improves the quality of the learned representations by devising multiple random classification tasks.
SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering
Scene graphs (SGs) have emerged as a useful tool for multimodal image analysis, showing impressive performance in tasks such as Visual Question Answering (VQA).
Self-supervised Learning of Contextualized Local Visual Embeddings
We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised convolutional-based method that learns representations suited for dense prediction tasks.
Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation
Our proposed model addresses this problem by providing an internal structure for the feature representations while extracting a global representation that supports the former.
RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping
Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs).
A deep learning approach to halo merger tree construction
A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure.
Representation Learning via Consistent Assignment of Views to Clusters
We introduce Consistent Assignment for Representation Learning (CARL), an unsupervised learning method to learn visual representations by combining ideas from self-supervised contrastive learning and deep clustering.
Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task
The semantic segmentation (SS) task aims to create a dense classification by labeling at the pixel level each object present on images.
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study
In this paper, we explore the effects of stacking databases, model initialization, and data amplification techniques when training with limited data on deep learning models' performance.
Hierarchical Transformer for Multilingual Machine Translation
The choice of parameter sharing strategy in multilingual machine translation models determines how optimally parameter space is used and hence, directly influences ultimate translation quality.
Consistent Assignment for Representation Learning
An unsupervised learning method to learn visual representations by combining contrastive learning with deep clustering.
Video Reenactment as Inductive Bias for Content-Motion Disentanglement
Experiments on video reenactment show the effectiveness of our disentanglement in the input space where our model outperforms the baselines in reconstruction quality and motion alignment.
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation
Anomaly detection suffers from unbalanced data since anomalies are quite rare.
Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
Crowd scene analysis has received a lot of attention recently due to the wide variety of applications, for instance, forensic science, urban planning, surveillance and security.
A Halo Merger Tree Generation and Evaluation Framework
Our aim is to provide a new framework for halo merger tree generation that takes advantage of the results of large volume simulations, with a modest computational cost.
Graph Learning Network: A Structure Learning Algorithm
We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions.
SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation
We evaluate state-of-the-art word embedding models for two languages using our proposed datasets for Tatar and the original datasets for English and report our findings on performance comparison.
