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Found 15 papers, 4 papers with code
High Resolution and Fast Face Completion via Progressively Attentive GANs
Face completion is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments.
Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection.
Modality-specific Distillation
In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.
On the Equivalence of Graph Convolution and Mixup
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales
Existing metrics like task performance of the LM generating the rationales, or similarity between generated and gold rationales are not good indicators of their human utility.
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
However, the bias inherent in user written text, often used for PTG model training, can inadvertently associate different levels of linguistic quality with users' protected attributes.
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available.
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior.
ER-Test: Evaluating Explanation Regularization Methods for Language Models
to align with human rationales (Which input tokens would humans focus on?).
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain.
BARACK: Partially Supervised Group Robustness With Guarantees
Theoretically, we provide generalization bounds for our approach in terms of the worst-group performance, which scale with respect to both the total number of training points and the number of training points with group labels.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding
The idea aims at mimicking a teacher's modality-specific predictions by introducing auxiliary loss terms for each modality.
Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs
The proposed frequency-oriented attentive module (FOAM) encourages GANs to attend to only finer details in the coarse-to-fine progressive training, thus enabling progressive attention to face structures.
High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks
It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments.
