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Found 25 papers, 14 papers with code
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data
Since our synthesizer only considers the generic properties of natural images, the single model pretrained on our dataset can be consistently transferred to various target datasets, and even outperforms the previous methods pretrained with the natural images in terms of Fr'echet inception distance.
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds
Existing WSSS methods commonly argue that the sparse coverage of CAM incurs the performance bottleneck of WSSS.
Weakly supervised segmentation
Weakly supervised Semantic Segmentation
+1
S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks
Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user.
Few-shot Font Generation with Weakly Supervised Localized Representations
Existing methods learn to disentangle style and content elements by developing a universal style representation for each font style.
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation
Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects.
Ranked #9 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Saliency Detection
Weakly supervised Semantic Segmentation
+1
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e. g., left-side sub-glyph.
Session-aware Linear Item-Item Models for Session-based Recommendation
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e. g., on e-commerce or multimedia streaming services.
Waste not, Want not: All-Alive Pruning for Extremely Sparse Networks
We demonstrate that AAP equipped with existing pruning methods (i. e., iterative pruning, one-shot pruning, and dynamic pruning) consistently improves the accuracy of original methods at 128× - 4096× compression ratios on three benchmark datasets.
GridMix: Strong regularization through local context mapping
Recently developed regularization techniques improve the networks generalization by only considering the global context.
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction
When a person identifies objects, he or she can think by associating objects to many classes and conclude by taking inter-class relations into account.
Few-shot Font Generation with Localized Style Representations and Factorization
However, learning component-wise styles solely from reference glyphs is infeasible in the few-shot font generation scenario, when a target script has a large number of components, e. g., over 200 for Chinese.
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
Rethinking the Truly Unsupervised Image-to-Image Translation
To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains.
Evaluating Weakly Supervised Object Localization Methods Right
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
Collaborative Distillation for Top-N Recommendation
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model.
Attention-based Dropout Layer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations.
Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera using Deep Residual Networks
We propose a novel approach to recovering the translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks.
Recycling the discriminator for improving the inference mapping of GAN
In order to analyze the real data in the latent space of GANs, it is necessary to investigate the inverse generation mapping from the data to the latent vector.
Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously
Also, we show that our model can achieve the competitive performance with the state-of-the-art attribute editing technique in terms of attribute editing quality.
Resembled Generative Adversarial Networks: Two Domains with Similar Attributes
We propose a novel algorithm, namely Resembled Generative Adversarial Networks (GAN), that generates two different domain data simultaneously where they resemble each other.
Generative Adversarial Networks for Unsupervised Object Co-localization
Our important finding is that high image diversity of GAN, which is a main goal in GAN research, is ironically disadvantageous for object localization, because such discriminators focus not only on the target object, but also on the various objects, such as background objects.
Discriminator Feature-based Inference by Recycling the Discriminator of GANs
Various studies assert that the latent space of a GAN is semanticallymeaningful and can be utilized for advanced data analysis and manipulation.
MGGAN: Solving Mode Collapse using Manifold Guided Training
Mode collapse is a critical problem in training generative adversarial networks.
Improved Techniques For Weakly-Supervised Object Localization
To this end, we employ an effective data augmentation for improving the accuracy of the object localization.
Improved Training of Generative Adversarial Networks Using Representative Features
Because the AE learns to minimize forward KL divergence, our GAN training with representative features is influenced by both reverse and forward KL divergence.
