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Found 10 papers, 2 papers with code
Paraphrasing Complex Network: Network Compression via Factor Transfer
Among the model compression methods, a method called knowledge transfer is to train a student network with a stronger teacher network.
Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation
Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network.
Broadcasting Convolutional Network for Visual Relational Reasoning
In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features.
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
In addition to this performance enhancement problem, we show that the proposed PGN can be adopted to solve the classical adversarial problem without utilizing the information on the target classifier.
FEED: Feature-level Ensemble Effect for knowledge Distillation
This paper proposes a versatile and powerful training algorithm named Feature-level Ensemble Effect for knowledge Distillation(FEED), which is inspired by the work of factor transfer.
FEED: Feature-level Ensemble for Knowledge Distillation
We name this method as parallel FEED, andexperimental results on CIFAR-100 and ImageNet show that our method has clear performance enhancements, without introducing any additional parameters or computations at test time.
Feature-map-level Online Adversarial Knowledge Distillation
By training a network to fool the corresponding discriminator, it can learn the other network's feature map distribution.
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective
In this paper, we focus on knowledge distillation and demonstrate that knowledge distillation methods are orthogonal to other efficiency-enhancing methods both analytically and empirically.
Local-Selective Feature Distillation for Single Image Super-Resolution
In this paper, we propose a novel feature distillation (FD) method which is suitable for SISR.
Semantics-Guided Object Removal for Facial Images: with Broad Applicability and Robust Style Preservation
U-net, a conventional approach for conditional GANs, retains fine details of unmasked regions but the style of the reconstructed image is inconsistent with the rest of the original image and only works robustly when the size of the occluding object is small enough.
