Search Results for author:
Found 35 papers, 3 papers with code
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions
However, such techniques suffer from their lack of adaptability to the target devices, as a hardware typically only support specific bit widths.
Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks
To solve this problem, a popular solution is DNN pruning, and more so structured pruning, where coherent computational blocks (e. g. channels for convolutional networks) are removed: as an exhaustive search of the space of pruned sub-models is intractable in practice, channels are typically removed iteratively based on an importance estimation heuristic.
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings
However, this led to an increase in the memory footprint, to a point where it can be challenging to simply load a model on commodity devices such as mobile phones.
MultIOD: Rehearsal-free Multihead Incremental Object Detector
The main challenge of incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one.
Gradient-Based Post-Training Quantization: Challenging the Status Quo
GPTQ essentially consists in learning the rounding operation using a small calibration set.
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
However, the optimization of the exponent parameter and weight values remains a challenging and novel problem which could not be solved with previous post training optimization techniques which only learn to round up or down weight values in order to preserve the predictive function.
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference
In this work, we propose to investigate DNN layer importance, i. e. to estimate the sensitivity of the accuracy w. r. t.
Designing strong baselines for ternary neural network quantization through support and mass equalization
We show experimentally that our approach allows to significantly improve the performance of ternary quantization through a variety of scenarios in DFQ, PTQ and QAT and give strong insights to pave the way for future research in deep neural network quantization.
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon large amounts of annotated data.
Fighting noise and imbalance in Action Unit detection problems
However, applying label smoothing as it is may aggravate imbalance-based pre-existing under-confidence issue and degrade performance.
PowerQuant: Automorphism Search for Non-Uniform Quantization
In this paper, we identity the uniformity of the quantization operator as a limitation of existing approaches, and propose a data-free non-uniform method.
Multi-Task Transformer with uncertainty modelling for Face Based Affective Computing
From a learning point of view we use an uncertainty weighted loss for modelling the difference of stochasticity between the three tasks annotations.
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks.
SPIQ: Data-Free Per-Channel Static Input Quantization
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community.
To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding
Batch-Normalization (BN) layers have become fundamental components in the evermore complex deep neural network architectures.
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition
Facial Expression Recognition (FER) is crucial in many research domains because it enables machines to better understand human behaviours.
Facial Expression Recognition
Facial Expression Recognition (FER)
Multi-label Transformer for Action Unit Detection
Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements.
Multi-Order Networks for Action Unit Detection
MONET uses a differentiable order selection to jointly learn task-wise modules with their optimal chaining order.
RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks
Deep Neural Networks (DNNs) are ubiquitous in today's computer vision landscape, despite involving considerable computational costs.
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging
Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration.
Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
Ranked #6 on
Overlapped 15-1
on PASCAL VOC 2012
Class Incremental Learning
Continual Semantic Segmentation
+5
RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks
Deep Neural Networks (DNNs) are ubiquitous in today's computer vision land-scape, despite involving considerable computational costs.
PLOP: Learning without Forgetting for Continual Semantic Segmentation
classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
Ranked #1 on
Domain 11-5
on Cityscapes val
Class Incremental Learning
Continual Semantic Segmentation
+16
THIN: THrowable Information Networks and Application for Facial Expression Recognition In The Wild
Thus, the exogenous information is used two times in a throwable fashion, first as a conditioning variable for the target task, and second to create invariance within the endogenous representation.
Facial Expression Recognition
Facial Expression Recognition (FER)
DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation
In this paper, we introduce a deep, end-to-end trainable ensemble of heatmap-based weak predictors for 2D/3D gaze estimation.
Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion
Head pose estimation and face alignment constitute a backbone preprocessing for many applications relying on face analysis.
Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment
Face alignment consists of aligning a shape model on a face image.
Tree-gated Deep Regressor Ensemble For Face Alignment In The Wild
Face alignment consists in aligning a shape model on a face in an image.
SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss
As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects.
The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network
Image completion is the problem of generating whole images from fragments only.
DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wild
Face Alignment is an active computer vision domain, that consists in localizing a number of facial landmarks that vary across datasets.
Ranked #22 on
Face Alignment
on WFLW
Face Alignment with Cascaded Semi-Parametric Deep Greedy Neural Forests
GNF appears as an ideal regressor for face alignment, as it combines differentiability, high expressivity and fast evaluation runtime.
Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection
Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data.
Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests
As such, our approach appears as a natural extension of Random Forests for learning spatio-temporal patterns, potentially from multiple viewpoints.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Pairwise Conditional Random Forests for Facial Expression Recognition
Facial expression can be seen as the dynamic variation of one's appearance over time.
Facial Expression Recognition
Facial Expression Recognition (FER)
