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Found 11 papers, 1 papers with code
Anti-aliasing Deep Image Classifiers using Novel Depth Adaptive Blurring and Activation Function
In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring.
A novel network training approach for open set image recognition
Convolutional Neural Networks (CNNs) are commonly designed for closed set arrangements, where test instances only belong to some "Known Known" (KK) classes used in training.
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition
Attention is a very popular and effective mechanism in artificial neural network-based sequence-to-sequence models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification
In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL.
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning
Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data.
Generalized Zero-Shot Learning
Generative Adversarial Network
Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation
In this work, we instill low-pass filtering into the AF (LP-ReLU) to improve robustness against HFc.
Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains.
Enhanced Transfer Learning with ImageNet Trained Classification Layer
Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task.
Transfer Learning Using Classification Layer Features of CNN
Although CNNs have gained the ability to transfer learned knowledge from source task to target task by virtue of large annotated datasets but consume huge processing time to fine-tune without GPU.
Distortion Robust Image Classification using Deep Convolutional Neural Network with Discrete Cosine Transform
Experimental results demonstrate that once trained, DCT-Net not only generalizes well to a variety of unseen image distortions but also outperforms other methods in the literature.
An Attention-Based Approach for Single Image Super Resolution
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.
