53 citations · 77 across the 10 of their papers we have counts for
10 papers
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is…
MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
Jinze Yu, Jiaming Liu, Xiaobao Wei +7
Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance. However, it requires large-scale labeled data and suffers from…
Contrastive Neural Processes for Self-Supervised Learning
Konstantinos Kallidromitis, Denis Gudovskiy, Kazuki Kozuka +2
Recent contrastive methods show significant improvement in self-supervised learning in several domains. In particular, contrastive methods are most effective where data augmentatio…
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Denis Gudovskiy, Shun Ishizaka, Kazuki Kozuka
Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the trai…
AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation
Denis Gudovskiy, Luca Rigazio, Shun Ishizaka +2
AutoAugment has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve gen…
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi +1
Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural networks (DNNs) by selecting the most representative data points for annotation. However, curr…
Smart Home Appliances: Chat with Your Fridge
Denis Gudovskiy, Gyuri Han, Takuya Yamaguchi +1
Current home appliances are capable to execute a limited number of voice commands such as turning devices on or off, adjusting music volume or light conditions. Recent progress in…
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi +2
Explaining predictions of deep neural networks (DNNs) is an important and nontrivial task. In this paper, we propose a practical approach to interpret decisions made by a DNN objec…
DNN Feature Map Compression using Learned Representation over GF(2)
Denis A. Gudovskiy, Alec Hodgkinson, Luca Rigazio
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Un…
ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks
Denis A. Gudovskiy, Luca Rigazio
In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-…