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Found 15 papers, 6 papers with code
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks
To remedy this problem, we propose a new paradigm for NAS that effectively reduces the use of memory while maintaining high performance.
Adversarial Robustness for Unsupervised Domain Adaptation
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
Distilling Global and Local Logits With Densely Connected Relations
In prevalent knowledge distillation, logits in most image recognition models are computed by global average pooling, then used to learn to encode the high-level and task-relevant knowledge.
Ranked #10 on
Knowledge Distillation
on ImageNet
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Multi-Attention Based Ultra Lightweight Image Super-Resolution
MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block.
Towards an Adversarially Robust Normalization Approach
In this paper, we investigate how BatchNorm causes this vulnerability and proposed new normalization that is robust to adversarial attacks.
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
We argue that such random selection strategies of the patches may not necessarily represent sufficient information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation.
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights
To solve this problem, we propose a hybrid weight representation (HWR) method which produces a network consisting of two types of weights, i. e., ternary weights (TW) and sparse-large weights (SLW).
A NEW POINTWISE CONVOLUTION IN DEEP NEURAL NETWORKS THROUGH EXTREMELY FAST AND NON PARAMETRIC TRANSFORMS
Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.
An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis
Based on SVWH, we propose a second ILWP and quantization method which quantize the predicted residuals between the weights in adjacent convolution layers.
Hybrid Residual Attention Network for Single Image Super Resolution
To address these issues, we present a binarized feature fusion (BFF) structure that utilizes the extracted features from residual groups (RG) in an effective way.
New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms
Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.
Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks
We present two FCN architectures for SIVG.
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks
Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3. 5 million frames of sharp and gradual transitions.
Ranked #1 on
Camera shot boundary detection
on ClipShots
