Implementations of few-shot object detection benchmarks
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Updated
Nov 21, 2023 - Python
Implementations of few-shot object detection benchmarks
code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"
Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
Official Pytorch implementation of ReBias (Learning De-biased Representations with Biased Representations), ICML 2020
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
Repository of the ICML 2020 paper "Set Functions for Time Series"
Soft Threshold Weight Reparameterization for Learnable Sparsity
Official implementation of the paper Stochastic Latent Residual Video Prediction
Benchmarking continual learning techniques for Human Activity Recognition data. We offer interesting insights on how the performance techniques vary with a domain other than images.
Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (ICML 2020)
Code repo for Gradient Temporal-Difference Learning with Regularized Corrections paper.
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)
Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020
Code-repository for the ICML 2020 paper Fairwashing explanations with off-manifold detergent
This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.
PyTorch Implementation of Momentum-Based Policy Gradient Methods
A Systematic Comparison of Robustness in Bayesian Deep Learning on Diabetic Retinopathy Diagnosis Tasks
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