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Found 30 papers, 16 papers with code
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.
Improved Techniques For Weakly-Supervised Object Localization
To this end, we employ an effective data augmentation for improving the accuracy of the object localization.
MGGAN: Solving Mode Collapse using Manifold Guided Training
Mode collapse is a critical problem in training generative adversarial networks.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 #22 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
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.
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.
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.
Ranked #17 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised segmentation
Weakly supervised Semantic Segmentation
+1
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.
SeiT++: Masked Token Modeling Improves Storage-efficient Training
A recent breakthrough by SeiT proposed the use of Vector-Quantized (VQ) feature vectors (i. e., tokens) as network inputs for vision classification.
Weakly Supervised Semantic Segmentation for Driving Scenes
Notably, the proposed method achieves 51. 8\% mIoU on the Cityscapes test dataset, showcasing its potential as a strong WSSS baseline on driving scene datasets.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Memory-Efficient Personalization using Quantized Diffusion Model
The rise of billion-parameter diffusion models like Stable Diffusion XL, Imagen, and Dall-E3 markedly advances the field of generative AI.
Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels
This study demonstrates a cost-effective approach to semantic segmentation using self-supervised vision transformers (SSVT).
Latent Inversion with Timestep-aware Sampling for Training-free Non-rigid Editing
In this work, we propose a training-free approach for non-rigid editing with Stable Diffusion, aimed at improving the identity preservation quality without compromising editability.
